{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "a6e161cd",
   "metadata": {
    "id": "a6e161cd"
   },
   "source": [
    "# Step 3: Use Reinforcement Learning to align SAWYER to preferred responses"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "YmpAXJoV3n4u",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "YmpAXJoV3n4u",
    "outputId": "90244881-4fd1-4518-c9eb-055bf210bb28"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Successfully installed GitPython-3.1.43 accelerate-0.29.3 bitsandbytes-0.43.1 datasets-2.19.0 dill-0.3.8 docker-pycreds-0.4.0 gitdb-4.0.11 huggingface-hub-0.22.2 multiprocess-0.70.16 nvidia-cublas-cu12-12.1.3.1 nvidia-cuda-cupti-cu12-12.1.105 nvidia-cuda-nvrtc-cu12-12.1.105 nvidia-cuda-runtime-cu12-12.1.105 nvidia-cudnn-cu12-8.9.2.26 nvidia-cufft-cu12-11.0.2.54 nvidia-curand-cu12-10.3.2.106 nvidia-cusolver-cu12-11.4.5.107 nvidia-cusparse-cu12-12.1.0.106 nvidia-nccl-cu12-2.19.3 nvidia-nvjitlink-cu12-12.4.127 nvidia-nvtx-cu12-12.1.105 peft-0.10.0 sentence-transformers-2.7.0 sentry-sdk-1.45.0 setproctitle-1.3.3 shtab-1.7.1 smmap-5.0.1 trl-0.8.5 tyro-0.8.3 wandb-0.16.6 xxhash-3.4.1\n"
     ]
    }
   ],
   "source": [
    "!pip install transformers[torch] datasets trl[peft] wandb peft bitsandbytes sentence-transformers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2b791f9e",
   "metadata": {
    "id": "2b791f9e"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd\n",
    "import torch\n",
    "from datasets import load_dataset, Dataset\n",
    "from transformers import AutoTokenizer, pipeline, AutoModelForSequenceClassification, AutoModelForCausalLM, BitsAndBytesConfig\n",
    "import time\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "from tqdm.auto import tqdm\n",
    "from trl import AutoModelForCausalLMWithValueHead, PPOConfig, PPOTrainer, set_seed, create_reference_model\n",
    "tqdm.pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0fce2770",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0fce2770",
    "outputId": "fde6388c-0772-4289-980a-24cfded359d9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "device(type='cuda')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n",
    "device"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a928d4d",
   "metadata": {
    "id": "9a928d4d"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3ebb479e",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 220,
     "referenced_widgets": [
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      "8cd0e82fb94b4951b76b777869e1b558",
      "095e5e9a3116493b90c74b5df096fdd9",
      "0f9cac04d37a4c89a3c06fdaecaf9ae2",
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      "d97807cb1f8247feb3d78f72fd0b6c64",
      "b7d4f85144244774a9e1a6e17889afa6",
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      "9955ef178700477aa00064e35748250a",
      "a74bb2ad92324b82a59f761520951363",
      "0b186af783804aa78777887a5fdf51ed"
     ]
    },
    "id": "3ebb479e",
    "outputId": "6f31da7d-b0d2-4444-fa40-0a25c039397e"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "12dbcd4f12df4b59936e01e8e8f6fbce",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading readme:   0%|          | 0.00/8.20k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d00ff79b82ad4d8d8955559f0dddf740",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Downloading data:   0%|          | 0.00/13.1M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "8cd0e82fb94b4951b76b777869e1b558",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Generating train split:   0%|          | 0/15011 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['instruction', 'context', 'response', 'category'],\n",
       "        num_rows: 15011\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dolly = load_dataset('databricks/databricks-dolly-15k')\n",
    "dolly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55e5121b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "55e5121b",
    "outputId": "dc8c0d8a-9375-4fe6-c3c3-93479824a9a3"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'instruction': 'When did Virgin Australia start operating?',\n",
       " 'context': \"Virgin Australia, the trading name of Virgin Australia Airlines Pty Ltd, is an Australian-based airline. It is the largest airline by fleet size to use the Virgin brand. It commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route. It suddenly found itself as a major airline in Australia's domestic market after the collapse of Ansett Australia in September 2001. The airline has since grown to directly serve 32 cities in Australia, from hubs in Brisbane, Melbourne and Sydney.\",\n",
       " 'response': 'Virgin Australia commenced services on 31 August 2000 as Virgin Blue, with two aircraft on a single route.',\n",
       " 'category': 'closed_qa'}"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dolly['train'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "GiINusM729IP",
   "metadata": {
    "id": "GiINusM729IP"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "DGWLooIh5evB",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "DGWLooIh5evB",
    "outputId": "a820e7b1-388c-4f52-cbf6-eb3aff6b834e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Token has not been saved to git credential helper. Pass `add_to_git_credential=True` if you want to set the git credential as well.\n",
      "Token is valid (permission: write).\n",
      "Your token has been saved to /root/.cache/huggingface/token\n",
      "Login successful\n"
     ]
    }
   ],
   "source": [
    "import huggingface_hub\n",
    "from google.colab import userdata\n",
    "\n",
    "huggingface_hub.login(token=userdata.get('HF_TOKEN').strip())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b6b0f3c3",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 169,
     "referenced_widgets": [
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     ]
    },
    "id": "b6b0f3c3",
    "outputId": "c60ec857-fbdb-4698-cc8e-3aada30d8fb8"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c4ef1ecdae384a70a85980bdfa83bcf2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer_config.json:   0%|          | 0.00/51.1k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0c6ccb0830124f4ea70ee581918e8ad6",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "tokenizer.json:   0%|          | 0.00/9.09M [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e3842b6fb93b4269be4d092d60440bd2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "special_tokens_map.json:   0%|          | 0.00/335 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Special tokens have been added in the vocabulary, make sure the associated word embeddings are fine-tuned or trained.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "('right', '<|end_of_text|>')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load our custom tokenizer\n",
    "\n",
    "username, repo_name = 'profoz', 'sawyer-llama-3'\n",
    "\n",
    "tokenizer = AutoTokenizer.from_pretrained(f\"{username}/{repo_name}\")\n",
    "\n",
    "tokenizer.padding_side, tokenizer.pad_token"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5299c16",
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    },
    "id": "b5299c16",
    "outputId": "0f02e9a7-38e3-437e-b9c7-a5550a093fb9"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b5bfe7b3986b43c9a0f415cae6b264d1",
       "version_major": 2,
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      },
      "text/plain": [
       "config.json:   0%|          | 0.00/654 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "97813c12faed4eb3af63bf9681335330",
       "version_major": 2,
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       "model.safetensors.index.json:   0%|          | 0.00/23.9k [00:00<?, ?B/s]"
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     },
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    {
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      "text/plain": [
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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    },
    {
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     },
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    },
    {
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
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       "model_id": "ae1ad903a94441728f4a8d7f9d9ed7fc",
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "eaf421ef5fa443eaa9187c50c6622e23",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "1516286a55ce447eb9d1ce56a341acf2",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "generation_config.json:   0%|          | 0.00/126 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Replaced token \"###STOP###\" (token id 128256) weight with weight for \"stop talking\"\n",
      "Replaced token \"###HUMAN###\" (token id 128257) weight with weight for \"The human said:\"\n",
      "Replaced token \"###BOT###\" (token id 128258) weight with weight for \"The assistant said:\"\n"
     ]
    }
   ],
   "source": [
    "STOP_TOKEN = '###STOP###'\n",
    "HUMAN_TOKEN = '###HUMAN###'\n",
    "BOT_TOKEN = '###BOT###'\n",
    "\n",
    "EXTRA_TOKENS = {\n",
    "    'stop_token': {\n",
    "        'token': STOP_TOKEN,\n",
    "        'replace_embedding_with': 'stop talking'\n",
    "    },\n",
    "    'human_token': {\n",
    "        'token': HUMAN_TOKEN,\n",
    "        'replace_embedding_with': 'The human said:'\n",
    "    },\n",
    "    'bot_token': {\n",
    "        'token': BOT_TOKEN,\n",
    "        'replace_embedding_with': 'The assistant said:'\n",
    "    }\n",
    "}\n",
    "\n",
    "base_model = 'meta-llama/Meta-Llama-3-8B'\n",
    "\n",
    "quant_config = BitsAndBytesConfig(\n",
    "    load_in_4bit=True,\n",
    "    bnb_4bit_quant_type=\"nf4\",\n",
    "    bnb_4bit_compute_dtype=torch.bfloat16,\n",
    "    bnb_4bit_use_double_quant=False,\n",
    ")\n",
    "\n",
    "model = AutoModelForCausalLM.from_pretrained(\n",
    "    base_model,\n",
    "    torch_dtype=torch.bfloat16,\n",
    "    quantization_config=quant_config,\n",
    "    device_map={\"\": 0}\n",
    ")\n",
    "\n",
    "model.resize_token_embeddings(len(tokenizer))\n",
    "\n",
    "for extra_token, extra_info in EXTRA_TOKENS.items():\n",
    "    token_id = tokenizer(extra_info['token'])['input_ids'][-1]\n",
    "    new_embedding = model.model.embed_tokens.weight.data[tokenizer(extra_info['replace_embedding_with'])['input_ids'][1:]].mean(dim=0, keepdim=True)\n",
    "    EXTRA_TOKENS[extra_token]['new_embedding'] = new_embedding\n",
    "    model.model.embed_tokens.weight.data[token_id] = EXTRA_TOKENS[extra_token]['new_embedding'].clone()\n",
    "    EXTRA_TOKENS[extra_token]['token_id'] = token_id\n",
    "    print(f\"Replaced token \\\"{extra_info['token']}\\\" (token id {token_id}) weight with weight for \\\"{extra_info['replace_embedding_with']}\\\"\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "vo2H9MuQLTBT",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "vo2H9MuQLTBT",
    "outputId": "85f29427-f359-4e0c-b48f-4aedbb0b4448"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([[ 1.3733e-03,  5.0964e-03, -3.0365e-03,  ...,  2.2888e-03,\n",
       "         -1.9531e-03, -1.7166e-05],\n",
       "        [-2.7313e-03,  1.9379e-03, -1.3733e-03,  ..., -5.1498e-05,\n",
       "         -1.3962e-03, -1.9836e-03],\n",
       "        [ 9.5367e-04, -1.3367e-02,  4.1771e-04,  ...,  2.5940e-03,\n",
       "          7.0496e-03,  4.1809e-03],\n",
       "        ...,\n",
       "        [-1.9775e-02, -8.9722e-03,  5.1880e-03,  ...,  2.2461e-02,\n",
       "          1.0803e-02,  8.2397e-03],\n",
       "        [-4.0894e-03, -6.9580e-03, -5.3406e-03,  ...,  4.5166e-03,\n",
       "          3.0670e-03,  3.9062e-03],\n",
       "        [-4.3030e-03, -4.1199e-03, -4.0283e-03,  ..., -2.1057e-03,\n",
       "         -4.9744e-03,  6.8665e-03]], device='cuda:0', dtype=torch.bfloat16)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.model.embed_tokens.weight.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Lb9GH4LjkE-c",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 119,
     "referenced_widgets": [
      "64a7f0f2509348bab1e04211128a96c0",
      "bc2501552c8240a7ace61917c3e2bf35",
      "1f2db5344d204fddb8a795f25f7efc67",
      "63c20a45e1324b48b363750c5659c5b5",
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      "d859f5c259dc4d3eb59016af63ec913f",
      "9e09a91cbc114fcd957370478c4d0443",
      "e66b5212e5364975ac8b2daaa4330426",
      "77bb3da1dd2e435eb3f87eac64aa5ac0",
      "678525d4040e4fd48726d07c81405b77",
      "e56fb6e0efa1457a967b370f4f0186fa",
      "ae2a57af4ce64132a36fcc87163bce24",
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      "21ce35617c6445999816eb401826211b",
      "96e047ea492940058ae0443a1b5bc9f0",
      "99a8c6ef6a74402a88aa8335adc48dc3",
      "84c2aaa129ce488292da912bfac0676a",
      "21085a7f131243ec992f6f82ff1c3c00",
      "2eae7cc310734c18b13274a1fda3c12d",
      "7615b7582d3f4c738d7aed76eb248dac",
      "727c89d1f3834e25bfd416dc718dfd38"
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    },
    "id": "Lb9GH4LjkE-c",
    "outputId": "c87e31fa-d100-438e-eab0-1210c75f6ae1"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "64a7f0f2509348bab1e04211128a96c0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "adapter_config.json:   0%|          | 0.00/653 [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "e56fb6e0efa1457a967b370f4f0186fa",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "adapter_model.safetensors:   0%|          | 0.00/2.32G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:The `device_map` argument is not provided. We will override the device_map argument. to set the entire model on the current device. If you want to set the model on multiple devices, please provide a custom `device_map` argument.\n"
     ]
    }
   ],
   "source": [
    "from peft import PeftModel, PeftConfig\n",
    "from transformers import AutoModelForCausalLM\n",
    "\n",
    "model = PeftModel.from_pretrained(model, 'profoz/sawyer-llama-3')\n",
    "\n",
    "model = AutoModelForCausalLMWithValueHead.from_pretrained(\n",
    "    model,\n",
    "    torch_dtype=torch.bfloat16,\n",
    "    is_trainable=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aTdtcitf4r4q",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "aTdtcitf4r4q",
    "outputId": "a0d9e9f7-b5d0-4d87-f441-71676f6ec5e9"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'###HUMAN### hi ###BOT### sup ###STOP###'"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def join_convo(conversation):\n",
    "    convo = ''''''\n",
    "    last_speaker = None\n",
    "    for speaker, message in conversation:\n",
    "        last_speaker = speaker\n",
    "        if speaker == 'human':\n",
    "            convo += f\"{EXTRA_TOKENS['human_token']['token']} {message} \"\n",
    "        elif speaker == 'assistant':\n",
    "            convo += f\"{EXTRA_TOKENS['bot_token']['token']} {message} \"\n",
    "    if last_speaker == 'human':\n",
    "        return convo.strip() + f\" {EXTRA_TOKENS['bot_token']['token']}\"\n",
    "    return convo.strip() + f\" {EXTRA_TOKENS['stop_token']['token']}\"\n",
    "\n",
    "join_convo(\n",
    "    [\n",
    "        ('human', 'hi'),\n",
    "        ('assistant', 'sup')\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "miMv4lIk46mM",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "miMv4lIk46mM",
    "outputId": "29b679ba-9a37-4418-a437-43bddd652174"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'###HUMAN### hi ###BOT###'"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "join_convo(\n",
    "    [\n",
    "        ('human', 'hi')\n",
    "    ]\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "uxW7UlUu49uy",
   "metadata": {
    "id": "uxW7UlUu49uy"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94174c8b",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 105,
     "referenced_widgets": [
      "ec3432902a554099beb90f5bd7e4d587",
      "95fac75e9a6b46a7a10f5cf84fe86f3c",
      "e2e86b4a043449a8a6a555024c3dd0a3",
      "1b83583a0cf34cab942a1ed0e806f7da",
      "08a2ab24d1314b34a5af51f5c8477941",
      "695941b2d2c0489daebd2ab37e611d1c",
      "b76951445acb4d5184cb0260208e3a87",
      "727d768099974f6ebe0c16d2cf8035a6",
      "66d634b4f1134fc8a8f4898a0fc8ec0a",
      "27e4a45941e44ab6941ae1a6d770d3ce",
      "e8106e215aa94182ad6b155dee172403"
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    },
    "id": "94174c8b",
    "outputId": "b9a4d0b7-dfc5-457f-b730-7e403e5fe023",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Parameter 'function'=<function preprocess_function at 0x7e2a2304da20> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n",
      "WARNING:datasets.fingerprint:Parameter 'function'=<function preprocess_function at 0x7e2a2304da20> of the transform datasets.arrow_dataset.Dataset._map_single couldn't be hashed properly, a random hash was used instead. Make sure your transforms and parameters are serializable with pickle or dill for the dataset fingerprinting and caching to work. If you reuse this transform, the caching mechanism will consider it to be different from the previous calls and recompute everything. This warning is only showed once. Subsequent hashing failures won't be showed.\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ec3432902a554099beb90f5bd7e4d587",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Map:   0%|          | 0/15011 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# quick cleaning function to clean some text\n",
    "def ascci(text): return ''.join(char if ord(char) < 128 else '?' for char in text)\n",
    "\n",
    "def preprocess_function(examples):\n",
    "    new_examples = {\n",
    "        \"input_ids\": [],\n",
    "        \"attention_mask\": [],\n",
    "        \"input_text\": [],\n",
    "        \"formatted_convo\": []\n",
    "    }\n",
    "    for instruction, context in zip(examples[\"instruction\"], examples['context']):\n",
    "        if context:\n",
    "            instruction = f'{instruction}\\n\\n{context}'\n",
    "\n",
    "        input_text = join_convo([['human', instruction]])\n",
    "\n",
    "        tokenized_question = tokenizer(input_text, return_tensors='pt')\n",
    "        new_examples['input_text'].append(input_text)\n",
    "        new_examples[\"input_ids\"].append(tokenized_question[\"input_ids\"])\n",
    "        new_examples[\"attention_mask\"].append(tokenized_question[\"attention_mask\"])\n",
    "        new_examples['formatted_convo'].append(input_text)\n",
    "    return new_examples\n",
    "\n",
    "dolly = dolly.map(preprocess_function, batched=True, batch_size=64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5ec82a2",
   "metadata": {
    "id": "a5ec82a2"
   },
   "outputs": [],
   "source": [
    "dolly.set_format(type=\"torch\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "wRPTisUUKSdE",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 592
    },
    "id": "wRPTisUUKSdE",
    "outputId": "4b2a710c-a6ae-453f-9a3b-71d55d603d1f"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(dolly['train']['category']).value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "TLdOttjUmGv0",
   "metadata": {
    "id": "TLdOttjUmGv0"
   },
   "outputs": [],
   "source": [
    "# dolly = dolly.filter(lambda x: 'qa' in x['category'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54fc57e1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 49,
     "referenced_widgets": [
      "9318edf1f79b4241b5175bb2450840ab",
      "62676f18ebbf49d698462b5a1d46ee80",
      "7116b0c287874da29642eb8cd1e5f836",
      "c19c0156bfb64c09843845484a0fb182",
      "cea6e56b8a1b4228850633d4076aa267",
      "3998af60aa6c40e8a366342a8f7a85e3",
      "10ee764e175c4ad6a6b1a4adaee9e884",
      "97a0442e8cb44eac903a8a70432dc0ec",
      "a454950b1ce54e24b5e3fbe2716bad30",
      "110fa21b942a42518eefbabaad46e234",
      "fcc57342e38946629f65ed96f0963c2f"
     ]
    },
    "id": "54fc57e1",
    "outputId": "99c3e29b-6d5b-4aa6-c5b2-e18c8c154155"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9318edf1f79b4241b5175bb2450840ab",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Filter:   0%|          | 0/15011 [00:00<?, ? examples/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dolly = dolly['train'].filter(\n",
    "    lambda x: x[\"input_ids\"].shape[-1] < 128, batched=False).shuffle(seed=42).select(range(2_000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fdh89yC_U61E",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 592
    },
    "id": "fdh89yC_U61E",
    "outputId": "c89426c3-f32a-4118-c9e3-db92ae7c22ef"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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REZHssKAQERGR7LCgEBERkewU+80CSd54DyIiIioNWFBIJ7CYERGVLjzFQ0RERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLKjL3UAItIcx4m7JHvuu8Hekj03EZV+PIJCREREslOkgrJ8+XLUrl0bSqUSSqUSnp6e2LNnj2p9RkYG/Pz8UL58eZQtWxY9evRAYmKi2r8RFxcHb29vmJqawsbGBuPGjUNWVlbx7A0RERFphSIVlMqVKyM4OBhnzpzB6dOn0aZNG3Tp0gWXL18GAIwePRo7duzAH3/8gfDwcDx48ADdu3dXfX92dja8vb3x8uVLREREYP369QgJCUFgYGDx7hURERGVakUag9KpUye1r2fPno3ly5fjxIkTqFy5MtasWYPQ0FC0adMGALBu3Tq4urrixIkTaNKkCfbv348rV67g4MGDsLW1Rd26dTFz5kxMmDAB06ZNg6GhYfHtGREREZVaHzwGJTs7G5s2bUJaWho8PT1x5swZvHr1Cu3atVNt4+LiAgcHB0RGRgIAIiMj4e7uDltbW9U2Xl5eSE1NVR2FyU9mZiZSU1PVHkRERKS9ilxQLl68iLJly8LIyAhDhgzBX3/9BTc3NyQkJMDQ0BCWlpZq29va2iIhIQEAkJCQoFZOctfnritIUFAQLCwsVI8qVaoUNTYRERGVIkUuKDVq1EB0dDSioqIwdOhQ+Pr64sqVK5rIphIQEICUlBTVIz4+XqPPR0RERNIq8jwohoaGqFatGgCgQYMGOHXqFBYtWoSvv/4aL1++RHJystpRlMTERNjZ2QEA7OzscPLkSbV/L/cqn9xt8mNkZAQjI6OiRiUiIqJS6qPnQcnJyUFmZiYaNGgAAwMDhIWFqdZdv34dcXFx8PT0BAB4enri4sWLSEpKUm1z4MABKJVKuLm5fWwUIiIi0hJFOoISEBCAjh07wsHBAc+ePUNoaCiOHDmCffv2wcLCAgMHDoS/vz+srKygVCoxYsQIeHp6okmTJgCA9u3bw83NDX379sXcuXORkJCAyZMnw8/Pj0dIiIiISKVIBSUpKQn9+vXDw4cPYWFhgdq1a2Pfvn34/PPPAQALFiyAnp4eevTogczMTHh5eeHnn39WfX+ZMmWwc+dODB06FJ6enjAzM4Ovry9mzJhRvHtFREREpVqRCsqaNWveud7Y2BjLli3DsmXLCtymatWq2L17d1GeloiIiHQM78VDREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyU6SCEhQUhIYNG8Lc3Bw2Njbo2rUrrl+/rrZNRkYG/Pz8UL58eZQtWxY9evRAYmKi2jZxcXHw9vaGqakpbGxsMG7cOGRlZX383hAREZFWKFJBCQ8Ph5+fH06cOIEDBw7g1atXaN++PdLS0lTbjB49Gjt27MAff/yB8PBwPHjwAN27d1etz87Ohre3N16+fImIiAisX78eISEhCAwMLL69IiIiolJNvygb7927V+3rkJAQ2NjY4MyZM2jRogVSUlKwZs0ahIaGok2bNgCAdevWwdXVFSdOnECTJk2wf/9+XLlyBQcPHoStrS3q1q2LmTNnYsKECZg2bRoMDQ2Lb++IiIioVPqoMSgpKSkAACsrKwDAmTNn8OrVK7Rr1061jYuLCxwcHBAZGQkAiIyMhLu7O2xtbVXbeHl5ITU1FZcvX/6YOERERKQlinQE5U05OTkYNWoUmjVrhlq1agEAEhISYGhoCEtLS7VtbW1tkZCQoNrmzXKSuz53XX4yMzORmZmp+jo1NfVDYxMREVEp8MFHUPz8/HDp0iVs2rSpOPPkKygoCBYWFqpHlSpVNP6cREREJJ0PKijDhw/Hzp07cfjwYVSuXFm13M7ODi9fvkRycrLa9omJibCzs1Nt8/ZVPblf527ztoCAAKSkpKge8fHxHxKbiIiISokiFRQhBIYPH46//voLhw4dgpOTk9r6Bg0awMDAAGFhYapl169fR1xcHDw9PQEAnp6euHjxIpKSklTbHDhwAEqlEm5ubvk+r5GREZRKpdqDiIiItFeRxqD4+fkhNDQUf//9N8zNzVVjRiwsLGBiYgILCwsMHDgQ/v7+sLKyglKpxIgRI+Dp6YkmTZoAANq3bw83Nzf07dsXc+fORUJCAiZPngw/Pz8YGRkV/x4SERFRqVOkgrJ8+XIAQKtWrdSWr1u3Dv379wcALFiwAHp6eujRowcyMzPh5eWFn3/+WbVtmTJlsHPnTgwdOhSenp4wMzODr68vZsyY8XF7QkRERFqjSAVFCPHebYyNjbFs2TIsW7aswG2qVq2K3bt3F+WpiYiISIfwXjxEREQkOywoREREJDssKERERCQ7LChEREQkOywoREREJDssKERERCQ7LChEREQkOywoREREJDssKERERCQ7LChEREQkOywoREREJDssKERERCQ7LChEREQkOywoREREJDssKERERCQ7LChEREQkOywoREREJDssKERERCQ7LChEREQkOywoREREJDssKERERCQ7LChEREQkOywoREREJDssKERERCQ7LChEREQkOywoREREJDssKERERCQ7LChEREQkOywoREREJDssKERERCQ7LChEREQkOywoREREJDssKERERCQ7LChEREQkOywoREREJDssKERERCQ7+lIHICIqbo4Td0n23HeDvSV7biJtwiMoREREJDssKERERCQ7LChEREQkOywoREREJDssKERERCQ7LChEREQkO0UuKEePHkWnTp1gb28PhUKBbdu2qa0XQiAwMBAVK1aEiYkJ2rVrh5s3b6pt8/TpU/j4+ECpVMLS0hIDBw7E8+fPP2pHiIiISHsUuaCkpaWhTp06WLZsWb7r586di8WLF2PFihWIioqCmZkZvLy8kJGRodrGx8cHly9fxoEDB7Bz504cPXoUgwcP/vC9ICIiIq1S5InaOnbsiI4dO+a7TgiBhQsXYvLkyejSpQsA4Ndff4WtrS22bduGXr164erVq9i7dy9OnToFDw8PAMCSJUvwxRdf4KeffoK9vf1H7A4RERFpg2IdgxITE4OEhAS0a9dOtczCwgKNGzdGZGQkACAyMhKWlpaqcgIA7dq1g56eHqKiovL9dzMzM5Gamqr2ICIiIu1VrAUlISEBAGBra6u23NbWVrUuISEBNjY2auv19fVhZWWl2uZtQUFBsLCwUD2qVKlSnLGJiIhIZkrFVTwBAQFISUlRPeLj46WORERERBpUrAXFzs4OAJCYmKi2PDExUbXOzs4OSUlJauuzsrLw9OlT1TZvMzIyglKpVHsQERGR9irWguLk5AQ7OzuEhYWplqWmpiIqKgqenp4AAE9PTyQnJ+PMmTOqbQ4dOoScnBw0bty4OOMQERFRKVXkq3ieP3+OW7duqb6OiYlBdHQ0rKys4ODggFGjRmHWrFmoXr06nJycMGXKFNjb26Nr164AAFdXV3To0AGDBg3CihUr8OrVKwwfPhy9evXiFTxEREQE4AMKyunTp9G6dWvV1/7+/gAAX19fhISEYPz48UhLS8PgwYORnJyM5s2bY+/evTA2NlZ9z8aNGzF8+HC0bdsWenp66NGjBxYvXlwMu0NERETaoMgFpVWrVhBCFLheoVBgxowZmDFjRoHbWFlZITQ0tKhPTURERDqiVFzFQ0RERLqFBYWIiIhkhwWFiIiIZIcFhYiIiGSHBYWIiIhkhwWFiIiIZKfIlxkTEZE8OU7cJdlz3w32luy5STvxCAoRERHJDgsKERERyQ4LChEREckOCwoRERHJDgsKERERyQ4LChEREckOCwoRERHJDgsKERERyQ4LChEREckOCwoRERHJDqe6JyKiUo1T/GsnHkEhIiIi2WFBISIiItnhKR4iIqJSSNtPbfEIChEREckOCwoRERHJDgsKERERyQ4LChEREckOCwoRERHJDgsKERERyQ4LChEREckOCwoRERHJDgsKERERyQ4LChEREckOCwoRERHJDgsKERERyQ4LChEREckOCwoRERHJDgsKERERyQ4LChEREckOCwoRERHJDgsKERERyQ4LChEREckOCwoRERHJDgsKERERyQ4LChEREcmOpAVl2bJlcHR0hLGxMRo3boyTJ09KGYeIiIhkQrKC8vvvv8Pf3x9Tp07F2bNnUadOHXh5eSEpKUmqSERERCQTkhWU+fPnY9CgQRgwYADc3NywYsUKmJqaYu3atVJFIiIiIpnQl+JJX758iTNnziAgIEC1TE9PD+3atUNkZGSe7TMzM5GZman6OiUlBQCQmpr6wRlyMtM/+Hs/1sfk/ljc75LH/S553O+Sx/0ueaVxv3O/Twjx/o2FBO7fvy8AiIiICLXl48aNE40aNcqz/dSpUwUAPvjggw8++OBDCx7x8fHv7QqSHEEpqoCAAPj7+6u+zsnJwdOnT1G+fHkoFIoSzZKamooqVaogPj4eSqWyRJ9bStxv7rcu4H5zv3WBlPsthMCzZ89gb2//3m0lKSgVKlRAmTJlkJiYqLY8MTERdnZ2ebY3MjKCkZGR2jJLS0tNRnwvpVKpU7/QubjfuoX7rVu437pFqv22sLAo1HaSDJI1NDREgwYNEBYWplqWk5ODsLAweHp6ShGJiIiIZESyUzz+/v7w9fWFh4cHGjVqhIULFyItLQ0DBgyQKhIRERHJhGQF5euvv8ajR48QGBiIhIQE1K1bF3v37oWtra1UkQrFyMgIU6dOzXPKSdtxv7nfuoD7zf3WBaVlvxVCFOZaHyIiIqKSw3vxEBERkeywoBAREZHssKAQERGR7LCgEBERkeywoBAREZHssKAQERGR7JSKe/HIwZYtW7B582bExcXh5cuXauvOnj0rUaqSk5GRkWe/tXVq6LS0NAQHByMsLAxJSUnIyclRW3/nzh2JkpEmFHRXVoVCASMjIxgaGpZwopJz+vTpAl/Xtm7dKlEq0pSwsLACX9fWrl0rUaqCsaAUwuLFizFp0iT0798ff//9NwYMGIDbt2/j1KlT8PPzkzqexqSnp2P8+PHYvHkznjx5kmd9dna2BKk077vvvkN4eDj69u2LihUrlvgNKaVy4cKFfJcrFAoYGxvDwcFB9hM7fQhLS8t3/owrV66M/v37Y+rUqdDT056Dzps2bUK/fv3g5eWF/fv3o3379rhx4wYSExPRrVs3qeNpjK5+AJk+fTpmzJgBDw+PUvO6xoJSCD///DNWrVqF3r17IyQkBOPHj4ezszMCAwPx9OlTqeNpzLhx43D48GEsX74cffv2xbJly3D//n2sXLkSwcHBUsfTmD179mDXrl1o1qyZ1FFKVN26dd/5omVgYICvv/4aK1euhLGxcQkm06yQkBDVB5BGjRoBAE6ePIn169dj8uTJePToEX766ScYGRnhxx9/lDht8ZkzZw4WLFgAPz8/mJubY9GiRXBycsL333+PihUrSh1PY3T1A8iKFSsQEhKCvn37Sh2l8AS9l4mJibh7964QQghra2sRHR0thBDixo0bwsrKSspoGlWlShVx+PBhIYQQ5ubm4ubNm0IIIX799VfRsWNHCZNplqOjo7hy5YrUMUrctm3bRI0aNcTq1avFhQsXxIULF8Tq1auFq6ur2LRpk/jvf/8rKleuLMaMGSN11GLVpk0b8fvvv+dZ/vvvv4s2bdoIIV7/zteoUaOko2mUqampiImJEUIIYWVlJS5cuCCEEOLKlSvCzs5OwmSaZWFhIY4dOyZ1jBJnZWUlbt26JXWMItGe45UaZGdnpzpS4uDggBMnTgAAYmJiILT4TgFPnz6Fs7MzgNfjTXL/HzRv3hxHjx6VMppGzZw5E4GBgUhPT5c6SomaPXs2Fi1ahIEDB8Ld3R3u7u4YOHAgFixYgHnz5sHHxwdLlizBX3/9JXXUYhUREYF69erlWV6vXj1ERkYCeP07HxcXV9LRNKpcuXJ49uwZAKBSpUq4dOkSACA5OVmrf/fLlSsHKysrqWOUuO+++w6hoaFSxygSnuIphDZt2mD79u2oV68eBgwYgNGjR2PLli04ffo0unfvLnU8jXF2dkZMTAwcHBzg4uKCzZs3o1GjRtixYwcsLS2ljqcx8+bNw+3bt2FrawtHR0cYGBiordfWQdEXL15E1apV8yyvWrUqLl68COD1aaCHDx+WdDSNqlKlCtasWZPntOWaNWtQpUoVAMCTJ09Qrlw5KeJpTIsWLXDgwAG4u7ujZ8+eGDlyJA4dOoQDBw6gbdu2UsfTmNwPIOvXr4epqanUcUpMRkYGVq1ahYMHD6J27dp5Xtfmz58vUbKCsaAUwqpVq1QDqfz8/FC+fHlERESgc+fO+P777yVOpzkDBgzA+fPn0bJlS0ycOBGdOnXC0qVL8erVK1n+MheXrl27Sh1BEi4uLggODsaqVatUV668evUKwcHBcHFxAQDcv39f9nccL6qffvoJPXv2xJ49e9CwYUMAr69uuXbtGrZs2QIAOHXqFL7++mspYxa7pUuXIiMjAwAwadIkGBgYICIiAj169MDkyZMlTqc5uvoB5MKFC6hbty4AqI6W5ZLrOBzezZgKLTY2FmfOnEG1atVQu3ZtqeNQMcst3Xp6eqqf78WLF5GdnY2dO3eiSZMm2LBhAxISEjBu3DiJ0xavmJgYrFy5Ejdu3AAA1KhRA99//z0cHR2lDUbFbvr06e9cP3Xq1BJKQu/DglIIBV1+mR++cWuPM2fO4OrVqwCAmjVr5jtOQds8e/YMGzduVHuj7tOnD8zNzSVORsWtoPlf8qOtcx7pqnv37gF4fQm9nLGgFIKent57D4EJIaBQKLRqbpDFixcXetsffvhBg0lKVlJSEnr16oUjR46oxtokJyejdevW2LRpE6ytraUNSMUuOTkZJ0+ezHdejH79+kmUSrN09XUtl659AMnJycGsWbMwb948PH/+HABgbm6OMWPGYNKkSbKc44cFpRC2bduGsWPHYty4cfD09AQAREZGYt68eZg7d67aL3Z+gwxLKycnJzx69Ajp6elqb9SmpqZqb9IKhUKrJjf6+uuvcefOHfz6669wdXUFAFy5cgW+vr6oVq0afvvtN4kTas7Nmzdx+PDhfN+oAwMDJUqlWTt27ICPjw+eP38OpVKp9qatUCi0dq6j9evXY+LEiejfv7/a69r69esRFBSkdnqrZcuWEqUsfrr6ASQgIABr1qzB9OnTVXM8HTt2DNOmTcOgQYMwe/ZsiRPmQ6LLm0uVhg0bil27duVZvmvXLlG/fn0JEpWMjRs3imbNmolr166pll27dk189tln4r///a+EyTRLqVSKkydP5lkeFRUlLCwsSj5QCVm1apUoU6aMsLW1FXXq1BF169ZVPerVqyd1PI2pXr26GDlypEhLS5M6Solq06aNCA0NzbN848aNomXLliUfqIR89dVXwsPDQ22uo8uXLwsPDw/Rq1cvCZNpVsWKFcXff/+dZ/m2bduEvb29BInejwWlEIyNjfOduOvKlSvC2NhYgkQlw9nZWZw9ezbP8tOnTwtHR0cJEpWMsmXLinPnzuVZfvbsWWFubl7ygUqIg4ODCA4OljpGiTM1NRW3b9+WOkaJMzExETdu3Miz/Pr168LExESCRCVDVz+AGBkZievXr+dZfu3aNdm+j8nvpJMMubq6IigoSO1mWi9fvkRQUJDqFIA2evjwIbKysvIsz87ORmJiogSJSkabNm0wcuRIPHjwQLXs/v37GD16tFbPD/HPP/+gZ8+eUscocV5eXjh9+rTUMUpclSpV8Msvv+RZvnr1atX8L9ooJycnz6XFwOtbObx9WlOb1KlTB0uXLs2zfOnSpahTp44Eid6PY1AK4eTJk+jUqROEEKqrdC5cuACFQoEdO3ao7t+hbTp16oT79+9j9erVqF+/PoDXA8sGDx6MSpUqYfv27RIn1Iz4+Hh07twZly9fVr1Qx8fHo1atWti+fbvsR75/qIEDB6Jhw4YYMmSI1FFK1Jo1azBjxgwMGDAA7u7ued68OnfuLFEyzdq9ezd69OiBatWqoXHjxgBev9bdvHkTf/75J7744guJE2pGly5dkJycjN9++w329vYAXn8A8fHxQbly5bRupuRc4eHh8Pb2hoODg9qYo/j4eOzevRufffaZxAnzYkEppLS0NGzcuBHXrl0D8PqoSp8+fWBmZiZxMs159OgRfH19sXfvXtWLdlZWFry8vBASEgIbGxuJE2qOEAIHDx5U+3m3a9dO4lSaFRQUhPnz58Pb2zvfN2ptulLrTe+6ekFbr2DJde/ePSxfvlx1NYurqyuGDBmi1UdQdPUDCAA8ePAAy5YtU3tdGzZsmKqoyQ0LSjHy9vbG6tWrte5OoDdv3lS9gLm4uODTTz+VOBFpgpOTU4HrtO1KLSq8YcOGYcaMGahQoYLUUYqNLn4AKY1YUIqRubk5zp8/r7rBnq5QKpWIjo4u1fu9ePFiDB48GMbGxu+d/0VbjyQQ5Ucb/r511YULF1CrVi3o6em9d8JROU4yyoJSjHS1oGjDfjs5OeH06dMoX748jyToABbSwtOGv29d/Xnr6ekhISEBNjY2qon58nvLl+upTBaUYqQNf8gfQlf3Wxv4+/tj5syZMDMzg7+//zu31aYbRLKQFp42/H3r6s87NjYWDg4OUCgUiI2Nfee2cpxklHczJnrLjBkzMHbs2Dy3Yn/x4gX+85//aNWMqufOncOrV69U/10Qud7t9EPFxMTk+9+knXT15/1m6YiNjUXTpk2hr6/+tp+VlYWIiAhZFhQeQSlG2vBJ40No236XKVMGDx8+zHOV0pMnT2BjYyPLQ6H04XSpkH4Ibfv71tWfd2l8XeNEbfTRtO3Ttfi/G6S97fz587CyspIgEWnS9OnTVTdPe1N6ejqmT58uQSLSJF39eRf0uvbkyRPZTpfBUzzF6Mcff9TJNzBtOQhXrlw5KBQKKBQKfPrpp2p/zNnZ2Xj+/LlWT2KWkZGBJUuWFHizwLNnz0qUTLNYSN/tm2++gVKplDpGsdG1n3f37t0BvP4g2b9/fxgZGanWZWdn48KFC2jatKlU8d6JBaWQCnOX14CAACmiSW7Pnj2oVKmS1DE+2sKFCyGEwLfffovp06fDwsJCtc7Q0BCOjo6qGRi10cCBA7F//37861//QqNGjbTuyNjbdLGQvu9S0zflXna6fPlyTcUpUbr48wageh0TQsDc3BwmJiaqdYaGhmjSpAkGDRokVbx34hiUQvjll18wdOhQVKhQAXZ2dnlux65NnyzfdyXHm7Tpqo43hYeHo2nTpvner0ObWVhYYPfu3apbsWu79evXqwrpwoULdaKQvnmp6fsKqBzHJHwMXfx5v2n69OkYO3asbE/n5IcFpRCqVq2KYcOGYcKECVJH0bjWrVsXajuFQoFDhw5pOI30MjIy1G4SCUCrDne/yc3NDZs2bZLlhE2apEuF9M1LTc+dO4exY8di3LhxavdmmTdvHubOnYuuXbtKlFKzwsPD0axZszxXs5D8sKAUAmdS1C3p6ekYP348Nm/ejCdPnuRZr22fLHPt2bMHixcvxooVK2R5yWFxSk1NVRXN1NTUd26rrYW0UaNGmDZtWp6bAu7evRtTpkzBmTNnJEqmWbt370aZMmXg5eWltnzfvn3IyclBx44dJUqmeVu2bMHmzZsRFxeX54OXHM8E8CqeQujZsyf2798vdQwqIePGjcOhQ4ewfPlyGBkZYfXq1Zg+fTrs7e3x66+/Sh1PYzw8PJCRkQFnZ2eYm5vDyspK7aFNypUrh6SkJACApaUlypUrl+eRu1xbXbx4Md9Jy5ycnHDlyhUJEpWMiRMn5vshQwiBiRMnSpCoZCxevBgDBgyAra0tzp07h0aNGqF8+fK4c+eObEsZj3EVQrVq1TBlyhScOHFCp+7yCgCnT58usHFv3bpVolSatWPHDvz6669o1aoVBgwYgM8++wzVqlVD1apVsXHjRvj4+EgdUSN69+6N+/fvY86cObC1tdXqQbKHDh1Sla7Dhw9LnEYarq6uCAoKwurVq2FoaAgAePnyJYKCguDq6ipxOs25efMm3Nzc8ix3cXHBrVu3JEhUMn7++WesWrUKvXv3RkhICMaPHw9nZ2cEBgbi6dOnUsfLn6D3cnR0LPDh5OQkdTyN+e2334SBgYH48ssvhaGhofjyyy/Fp59+KiwsLET//v2ljqcxZmZmIjY2VgghRKVKlURUVJQQQog7d+4IMzMzKaNplImJiYiOjpY6Rol69eqVmD59uoiPj5c6SomLiooSNjY2wtraWrRt21a0bdtWWFtbCxsbG9XvvDaytbUVYWFheZYfOHBAWFtbS5CoZJiYmIi7d+8KIYSwtrZW/a3fuHFDWFlZSRmtQDzFUwgxMTEFPrTpvg1vmzNnDhYsWIAdO3bA0NAQixYtwrVr1/DVV1/BwcFB6nga4+zsrJoO28XFBZs3bwbw+siKpaWlhMk0y8XFBS9evJA6RonS19fHf/7zH2RlZUkdpcQ1atQId+7cwaxZs1C7dm3Url0bs2fPxp07d9CoUSOp42lMly5dMGrUKNy+fVu17NatWxgzZgw6d+4sYTLNsrOzUx0pcXBwwIkTJwC8fn8Tch2KKnVDKk0yMzPFtWvXxKtXr6SOUiJMTU1FTEyMEEIIKysrceHCBSGEEFeuXBF2dnYSJtOs+fPni0WLFgkhXn+qMjY2FkZGRkJPT08sXLhQ4nSas2/fPtG0aVNx+PBh8fjxY5GSkqL20FadO3cWISEhUsegEpKcnCyaNGki9PX1VUfC9fX1RevWrcU///wjdTyNGThwoJg2bZoQQoilS5cKExMT0a5dO2FpaSm+/fZbidPlj1fxFEJ6ejpGjBiB9evXAwBu3LgBZ2dnjBgxApUqVdLagVWVK1fGnj174O7ujtq1ayMgIAC9e/dGZGQkOnTogJSUFKkjlojY2FicOXMG1apV0+pLcPX0Xh9QfXvsifi/OTO09eqlFStWYPr06fDx8UGDBg3yzBOhzZ+qN2zYgJUrV+LOnTuIjIxE1apVsWDBAjg7O6NLly5Sx9MYIQQOHDiA8+fPw8TEBLVr10aLFi2kjqVROTk5yMnJUV1evWnTJkRERKB69er4/vvvVeOQ5IQFpRBGjhyJ48ePY+HChejQoQMuXLgAZ2dn/P3335g2bdo77wJbmvXp0wceHh7w9/fHzJkzsWTJEnTp0gUHDhxA/fr1tXaQrK4KDw9/5/qWLVuWUJKSlVvM8qPNxWz58uUIDAzEqFGjMGvWLFy+fBnOzs4ICQnB+vXrdXbwMMkHC0ohVK1aFb///juaNGmidmfPW7duoX79+u+dR6G0evr0KTIyMmBvb4+cnBzMnTtX1bgnT56stZdg/vDDD6hWrVqeq7OWLl2KW7duYeHChdIE06BXr16hQ4cOWLFiBapXry51HCoBbm5umDNnDrp27ar2unbp0iW0atUKjx8/ljqixqSlpSE8PDzfqxO19apMZ2dntGzZEitWrFC7H8/jx49V45HkhpcZF8KjR4/y3KIaeP1Lrq2XYmZlZWHnzp2qyYz09PS09lTW2/78809s3749z/KmTZsiODhYKwuKgYFBke7Tok0yMjJgbGwsdYwSFxMTg3r16uVZbmRkhLS0NAkSlYxz587hiy++QHp6OtLS0mBlZYXHjx/D1NQUNjY2WltQ7t69C319fXz22WfYvn077OzsALyeePLNGYblhFfxFIKHhwd27dql+jq3lKxevVpr792gr6+PIUOGICMjQ+ooJe7Jkydq9+nIpVQqtfpT5TfffIM1a9ZIHaPEWVpaokWLFpgyZQrCwsJ05komJycnREdH51m+d+9erZ4HZfTo0ejUqRP++ecfmJiY4MSJE4iNjUWDBg3w008/SR1PYxQKBfbu3YvKlSujQYMGOHXqlNSR3otHUAphzpw56NixI65cuYKsrCwsWrQIV65cQURExHvP25dmjRo1QnR0tNZPe/62atWqYe/evRg+fLja8j179mj17Q6ysrKwdu1aHDx4MN/Botp6c8iDBw/i6NGjOHLkCBYsWICsrCx4eHigZcuWaNWqFT7//HOpI2qEv78//Pz8kJGRASEETp48id9++001eZu2io6OxsqVK6Gnp4cyZcogMzMTzs7OmDt3Lnx9fdG9e3epI2qEEAJly5bF1q1bERAQgJYtW2LVqlWy/v1mQSmE5s2bIzo6GsHBwXB3d8f+/ftRv359REZGwt3dXep4GjNs2DD4+/sjPj4+3zcsbb2ixd/fH8OHD8ejR4/Qpk0bAEBYWBjmzZunlad3cl26dAn169cH8PpKtTdp66lM4PXfd/PmzfHjjz8iKysLp06dwsqVKzF37lwEBwdr7SDZ7777DiYmJpg8eTLS09PRp08f2NvbY9GiRejVq5fU8TTGwMBANTDaxsYGcXFxcHV1hYWFBeLj4yVOpzlv/g0HBQWhZs2aGDRoEHr37i1hqnfjIFkqUH5XN7x5q3ZtfeEGXl/hMHv2bDx48AAA4OjoiGnTpqFfv34SJyNNuHHjBo4cOaJ6ZGZmokWLFmjVqhVGjhwpdTyNS09Px/Pnz/Mda6dt2rdvj/79+6NPnz4YNGgQLly4gB9++AEbNmzAP//8g6ioKKkjaoSenh4SEhLUfsaRkZHo1q0bHj16JMvXcxaUQsrOzsZff/2Fq1evAng9Ar5Lly5afcvu9w2c0oVTP48ePYKJiQnKli0rdZQSde/ePQCv58LRdpUqVcKLFy/QqlUrtGrVCi1btkTt2rW1+qgRALx48QJCCJiamgJ4/ff+119/wc3NDe3bt5c4neacPn0az549Q+vWrZGUlIR+/fqprk5cu3Yt6tSpI3XEEpWYmIhr167JchoBFpRCuHz5Mjp37oyEhATUqFEDwOtPXNbW1tixYwdq1aolcUKij5eTk4NZs2Zh3rx5eP78OQDA3NwcY8aMwaRJk945X0hpVrduXVy7dg3169dXlZTmzZur3ri1Vfv27dG9e3cMGTIEycnJqFGjBgwNDfH48WPMnz8fQ4cOlTpisRNCID4+HjY2Njp55VZpw4JSCJ6enrC2tsb69etVc3/8888/6N+/Px49eoSIiAiJE2rOhg0bsGLFCsTExKhmmly4cCGcnJy0aqbJ+vXrIywsDOXKlUO9evXe+en57NmzJZis5AQEBGDNmjWYPn06mjVrBgA4duwYpk2bhkGDBmH27NkSJ9Sc5ORkHD16FOHh4QgPD8eVK1dQt25dtG7dWmv3u0KFCggPD0fNmjWxevVqLFmyBOfOncOff/6JwMBA1dFibZKTkwNjY2NcvnxZJ+b7Ke2va9p7fqIYRUdH4/Tp02oTk5UrVw6zZ89Gw4YNJUymWW/ONDl79mzVOUpLS0ssXLhQqwpKly5dVJMXde3aVdowElm/fj1Wr16tNrV77dq1UalSJQwbNkxr36iB17/TnTt3RrNmzdC0aVP8/fff+O233xAVFaW1+52eng5zc3MAwP79+9G9e3fo6emhSZMmsp0X42Pp6emhevXqePLkiU4UlFL/ulaid/4ppWrXrp3v7bnDwsJErVq1JEhUMlxdXcVff/0lhBCibNmy4vbt20IIIS5evCjKly8vYbLit2jRIvHixQshhBCxsbEiOztb4kQlz8jISFy/fj3P8mvXrgljY2MJEpWMP//8U4wYMUK4u7uLMmXKCGtra9GtWzexaNEi1S3ptZG7u7tYtGiRiIuLE0qlUkRERAghhDh9+rSwtbWVOJ3mbN++XTRv3lxcvHhR6iglJisrS4SHh5e6myGyoBTCrl27RM2aNcUff/wh4uPjRXx8vPjjjz+Eu7u72LVrl9be8dXY2FjcvXtXCKFeUG7cuKF1b1hlypQRiYmJQggh9PT0VP+tSxo1aiRGjBiRZ/nw4cNF48aNJUhUMqytrUWPHj3EkiVLVHfs1gV//PGHMDAwEHp6eqJdu3aq5XPmzBEdOnSQMJlmWVpaCkNDQ6GnpyeMjY1FuXLl1B7aysjISNy5c0fqGEXCUzyF8OWXXwIAvvrqK9U5PPF/Q3c6deqk+lrbLr3NnWny7at1tHGmSXt7e/z555/44osvIITAvXv3CpxF18HBoYTTlYy5c+fC29sbBw8eVM2QHBkZifj4eOzevVvidJqTlJRUqO2Cg4MxZMgQWFpaajZQCfnXv/6F5s2b4+HDh2pXrrRt2xbdunWTMJlmLViwQOuv0MpPrVq1cOfOHTg5OUkdpdA4SLYQijJbrBwv1fpQq1evxrRp0zBv3jwMHDgQq1evxu3bt1UzTWrTZE6rVq3CiBEjkJWVVeA22lhC3/bgwQMsW7YM165dAwC4urpi2LBhsLe3lziZ9JRKJaKjo7VyNmFduqxcV+3duxcBAQGYOXNmvhNvKpVKiZIVjAWlkJKTk7FmzRq1eVAGDhyY7z1btMnGjRsxbdo03L59G8DrIw3Tp0/HwIEDJU5W/J49e4bY2FjUrl0bBw8eRPny5fPdTlvnSYiLi0OVKlXy/XQZFxentUeOCuvNO/5qA129rLxMmTJ4+PBhnknpnjx5AhsbG639APLmz/PNv3E5f/BiQSmE06dPo0OHDjA2NkajRo0AAKdOncKLFy9U095rO12aaXL9+vXo1auX2i3JdYGuvnAXlrYVFF29rDy/GVWB10cPP/nkE629WeT7zgTI8eg/C0ohfPbZZ6hWrRp++eUX1cyxWVlZ+O6773Dnzh0cPXpU4oREH09PTw+JiYmwtrZWWx4bGws3NzekpaVJlEwetK2g2NvbY8WKFWqXlQPA33//jWHDhuH+/fsSJdOMxYsXA3h9N+OZM2eqzQ6dnZ2No0eP4u7duzh37pxUEektHCRbCKdPn1YrJwCgr6+P8ePHw8PDQ8JkmpWYmIixY8ciLCwMSUlJeLvLatMnaisrK9y4cQMVKlRAuXLl3jmI7unTpyWYTPP8/f0BvD7sO2XKFLUZVLOzsxEVFYW6detKlI405enTp3Bxccmz3MXFRet+x4HXg2OB16c0VqxYgTJlyqjWGRoawtHREStWrJAqXolJT09HXFwcXr58qbZcjjd/ZUEpBKVSibi4uDx/zPHx8aqJjrRR//79ERcXhylTpqBixYpaPfJ9wYIFqp+lro3yz/3EKITAxYsXYWhoqFpnaGiIOnXqYOzYsVLFIw2pU6cOli5dqjqykGvp0qVaOc4qJiYGANC6dWts3bpVbeJNXfDo0SMMGDAAe/bsyXe9LD9wlvyVzaXPiBEjROXKlcWmTZtEXFyciIuLE7/99puoXLmyGDlypNTxNKZs2bLi3LlzUsegEtK/f3+tm8unOHXs2FE8ePBA6hjF5siRI8LMzEy4urqKb7/9Vnz77bfC1dVVlC1bVhw9elTqeBqTOyFjfrTp5/u2Pn36iGbNmolTp04JMzMzsX//frFhwwZRo0YNsXPnTqnj5YsFpRAyMzPFDz/8oJrcR09PTxgZGYlRo0aJjIwMqeNpjKurqzh79qzUMUrcmTNn1Cbs2rZtm+jSpYsICAgQmZmZEiYrWSkpKeKvv/4SV69elTqKxt26dUtMmjRJ9OrVSzVJ3+7du8WlS5ckTqZZ9+/fFz/++KPo3r276N69u5g0aZK4f/++1LE0ytXVNd8PXlu2bBEVKlQo+UAlxM7OTkRFRQkhhDA3N1fNGv3333+LZs2aSRmtQCwoRZCWliYuXLggLly4INLS0qSOo3H79u0T7du3FzExMVJHKVEeHh5iy5YtQgghbt++LYyMjETv3r1FtWrVtPqIWc+ePcWSJUuEEEKkp6eL6tWrCwMDA6Gvr6/6/6GNjhw5IkxMTES7du2EoaGhasbkoKAg0aNHD4nTUXEbOnSoMDIyEsHBwUIIIZ4/fy58fX2FiYmJmD9/vsTpNMfc3Fz1Wu7g4CCOHTsmhBDizp07wsTERMJkBeNVPFSgcuXKIT09HVlZWTA1NYWBgYHaem0cSAcAFhYWOHv2LD755BP8+9//xqFDh7Bv3z4cP34cvXr1Qnx8vNQRNcLOzg779u1DnTp1EBoaiqlTp+L8+fNYv349Vq1apbVXN3h6eqJnz57w9/dXu1Ln5MmT6N69u2oSM21w4cKFQm8rx0GTxWXXrl347rvvUK1aNTx8+BBly5bFf//7X9SqVUvqaBrTsGFDzJo1C15eXujcuTMsLS0RFBSExYsXY8uWLaq5ruSEg2SpQAsXLpQ6giSEEMjJyQEAHDx4UHWrgypVquDx48dSRtOolJQUWFlZAXg962SPHj1gamoKb29vjBs3TuJ0mnPx4kWEhobmWW5jY6N1P++6detCoVDkuSLvbXKduKu4dOzYEd27d8fy5cuhr6+PHTt2aHU5AYCRI0fi4cOHAICpU6eiQ4cO2LhxIwwNDRESEiJtuAKwoFCBfH19pY4gCQ8PD8yaNQvt2rVDeHg4li9fDuD1VQC2trYSp9OcKlWqIDIyElZWVti7dy82bdoEAPjnn39gbGwscTrNsbS0xMOHD/Pco+TcuXOoVKmSRKk0I/dKFl12+/Zt9OnTBwkJCdi3bx/Cw8PRuXNnjBw5ErNnz85zpFhbfPPNN6r/btCgAWJjY3Ht2jU4ODigQoUKEiYrmHbOZUzF5vbt25g8eTJ69+6tuqnanj17cPnyZYmTac7ChQtx9uxZDB8+HJMmTUK1atUAAFu2bEHTpk0lTqc5o0aNgo+PDypXrgx7e3u0atUKAHD06FG4u7tLG06DevXqhQkTJiAhIQEKhQI5OTk4fvw4xo4di379+kkdr1hVrVpV9QgNDUVYWJjasqpVqyIsLExVTrVR3bp14eTkhPPnz+Pzzz/HrFmzcPjwYWzdulU1U7guMDU1Rf369WVbTgDOJEvvEB4ejo4dO6JZs2Y4evQorl69CmdnZwQHB+P06dPYsmWL1BFLVEZGBsqUKaO1n7CA15MSxsfH4/PPP1fNtLlr1y5YWlqqpkPXNi9fvoSfnx9CQkKQnZ0NfX19ZGdno0+fPggJCVGb0EubODo6IjQ0NE/pjoqKQq9evbT2aMuGDRvQt2/fPMufPXuGUaNGYc2aNRKk0jwhBLZs2YLDhw8jKSlJdRo719atWyVKVjAWFCqQLg0efFN8fDwUCoXqzq4nT55EaGgo3NzcMHjwYInTkabExcXh0qVLeP78OerVq4fq1atLHUmjjI2NcfXq1Tyntu7cuQM3NzdkZGRIlKxkvHz5EjExMfjkk0/UZgnXViNHjsTKlSvRunVr2Nra5pmMct26dRIlK5j2/1Tog+nS4ME39enTB4MHD0bfvn2RkJCAzz//HDVr1sTGjRuRkJCAwMBAqSNqRHZ2NkJCQlS3Nnj7E9ahQ4ckSqZZx44dQ/PmzeHg4KBTd2yuUqUKjh8/nqegHD9+HPb29hKl0rwXL15g+PDhWL9+PQDgxo0bcHZ2xogRI1C5cmVMmDBB4oSasWHDBmzduhVffPGF1FEKjWNQqEC5gwffpo2DB9906dIl1bnozZs3o1atWoiIiMDGjRtlO9q9OIwcORIjR45EdnY2atWqhTp16qg9tFWbNm3g5OSEH3/8EVeuXJE6TokZNGgQRo0ahXXr1iE2NhaxsbFYu3YtRo8ejUGDBkkdT2MmTpyI8+fP48iRI2qDv9u1a6fVY28sLCxK340uJZuBhWRvzJgxonnz5uLhw4fC3Nxc3Lx5Uxw7dkw4OzuLadOmSR1PY8zMzFQTGnXq1Ek1oVNsbKwwNjaWMJlmlS9fXuzatUvqGCXu0aNHYsmSJaJp06ZCoVCIOnXqiLlz54r4+Hipo2lUTk6OGD9+vDA2NlbNkG1qaiqmT58udTSNcnBwEJGRkUKI17fzyJ2Y7+bNm8Lc3FzKaBoVEhIievXqJdLT06WOUmgcg0IFym/wYFZWFnx8fLR68GDjxo3RunVreHt7o3379jhx4gTq1KmDEydO4F//+pfWjr2xt7fHkSNH8Omnn0odRTIxMTEIDQ3Fb7/9hmvXrqFFixZae2or1/Pnz3H16lWYmJigevXqMDIykjqSRpmamuLSpUtwdnZWG1t3/vx5tGjRAikpKVJH1IgXL16gW7duOH78OBwdHfMM9j979qxEyQrGgkLvFR8fj4sXL+rM4MEjR46gW7duSE1Nha+vL9auXQsA+PHHH3Ht2jVZjnYvDvPmzcOdO3ewdOlSnbqb89uys7OxZ88eTJkyBRcuXNDqCct0UYsWLdCzZ0+MGDEC5ubmuHDhApycnDBixAjcvHkTe/fulTqiRnz11Vc4fPgw/vWvf+U7SHbq1KkSJSsYCwoVyN/fP9/lCoUCxsbGqFatGrp06aKafVSbZGdnIzU1Ve2W7Hfv3oWpqSlsbGwkTKY53bp1w+HDh2FlZYWaNWvm+YSlrcUs1/Hjx7Fx40Zs2bIFGRkZ6NKlC3x8fNChQwepo1ExOnbsGDp27IhvvvkGISEh+P7773HlyhVEREQgPDwcDRo0kDqiRpiZmWHfvn1o3ry51FEKjQWFCtS6dWucPXsW2dnZqFGjBoDXI97LlCkDFxcXXL9+HQqFAseOHYObm5vEaeljDRgw4J3r5XgZYnEICAjApk2b8ODBA3z++efw8fFBly5dYGpqKnU00pDbt28jODgY58+fx/Pnz1G/fn1MmDBBqyckdHFxwebNm0vVPZZYUKhACxcuxP/+9z+sW7cOSqUSwOv7tXz33Xdo3rw5Bg0ahD59+uDFixfYt2+fxGmL15YtW7B582bExcXh5cuXauvkeK6WPlyzZs3g4+ODr776StazalLJCg4OxpAhQ2BpaSl1lGKxa9cuLFmyBCtWrICjo6PUcQqFBYUKVKlSJRw4cCDP0ZHLly+jffv2uH//Ps6ePYv27dtr1bwoixcvxqRJk9C/f3+sWrUKAwYMwO3bt3Hq1Cn4+flh9uzZUkckIg1TKpWIjo4ufZfmFqA03p2eE7VRgVJSUpCUlJSnoDx69AipqakAXs+V8vYRhtLu559/xqpVq9C7d2+EhIRg/PjxcHZ2RmBgoCz/iD9G/fr1ERYWhnLlyqFevXrvHByrTUeOtm/fjo4dO8LAwADbt29/57adO3cuoVQkJ9r22b003p2eBYUK1KVLF3z77beYN28eGjZsCAA4deoUxo4di65duwJ4PQ28tl2WGhcXp7o/iYmJCZ49ewYA6Nu3L5o0aYKlS5dKGa9YdenSRXVZae7PVBd07doVCQkJsLGxeed+KxQKXsVDWqE03p2eBYUKtHLlSowePRq9evVCVlYWAEBfXx++vr5YsGABgNcDr1avXi1lzGJnZ2eHp0+fomrVqnBwcFDNgxITE6N1n6revLRQjpcZasqb0/i/PaU/kTZLSkrK91YWchw8yzEo9F7Pnz/HnTt3AADOzs6qu9xqq++++w5VqlTB1KlTsWzZMowbNw7NmjXD6dOn0b17d62926mu+vXXX/H111/nmaDs5cuX2LRpE/r16ydRMpLSm5O4aYMzZ87A19cXV69ezfNBS65HCllQiN6Sk5ODnJwc1R1ON23ahIiICFSvXh3ff/89DA0NJU6oGdnZ2ViwYEGBVy9p2/ibXGXKlMHDhw/zzG/z5MkT2NjYyPKFmzRP2wpKnTp18Mknn2DChAn5TtRWtWpViZIVjKd4iN6ip6cHPb3/fx/NXr16oVevXhImKhnTp0/H6tWrMWbMGEyePBmTJk3C3bt3sW3bNq29gzPwejBkfoOD7927BwsLCwkSkRx89tlnMDExkTpGsblz5w7+/PNPVKtWTeoohcYjKEQALly4UOht5Xiutjh88sknWLx4Mby9vWFubo7o6GjVshMnTiA0NFTqiMUq96ql8+fPo2bNmqojZsDro0kxMTHo0KEDNm/eLGFK0oScnBzcunUr37EYLVq0kCiVZnXt2hV9+/ZFjx49pI5SaDyCQgSgbt26UCgU7x0EK9dztcUhISFBNZNm2bJlVTdN+/LLLzFlyhQpo2lE7tU70dHR8PLyUhtbZWhoCEdHx1L1Yk6Fc+LECfTp0wexsbGlZixGcVi9ejV8fX1x6dIl1KpVK888KHK8nJ4FhQiv72Kr6ypXroyHDx/CwcEBn3zyCfbv34/69evj1KlTWnmH29yrlhwdHfH111/D2NhY4kRUEoYMGQIPDw/s2rULFStW1JkbY0ZGRuL48ePYs2dPnnVyLWY8xUP0lqCgINja2uLbb79VW7527Vo8evQIEyZMkCiZZk2cOBFKpRI//vgjfv/9d3zzzTdwdHREXFwcRo8ejeDgYKkjEn00MzMznD9/vlSNxSgOjo6OqqOhtra2UscpFBYUorc4OjoiNDRUNVlbrqioKPTq1UtnjracOHFCdfVSp06dpI6jMbp69ZKuatOmDcaPH69zd6l+c1xZacFTPERvSUhIQMWKFfMst7a2xsOHDyVIpHmvXr3C999/jylTpsDJyQkA0KRJEzRp0kTiZJqnq1cv6aoRI0ZgzJgxqjFXb4/F0NZB8N27d8fhw4dLVUHhERSit1SvXh1Tp07FN998o7Z8w4YNmDp1qmrSOm1jYWGB6OhoVUHRFbp29ZKue3MKgVy5A+TlOhajOMyePRsLFy6Et7d3vsXshx9+kChZwXgEhegtgwYNwqhRo/Dq1Su0adMGABAWFobx48djzJgxEqfTnK5du2Lbtm0YPXq01FFKlK5dvaTrdOUU7dtWr16NsmXLIjw8HOHh4WrrFAoFCwpRaTBu3Dg8efIEw4YNU41HMDY2xoQJExAQECBxOs2pXr06ZsyYgePHj6NBgwYwMzNTWy/HF7DioGtXL+k6Oc6YWhJKYzHjKR6iAjx//hxXr16FiYkJqlevrvVvVu86taNQKLT21BavXtI9t2/fxsKFC3H16lUAgJubG0aOHFmqxmfoAhYUIsoj92VBV+aIeFNkZCQiIyO1/uolXbVv3z507twZdevWRbNmzQAAx48fx/nz57Fjxw58/vnnEifUjLenTXjb2rVrSyhJ4bGgEJHKmjVrsGDBAty8eRPA69M+o0aNwnfffSdxMqLiUa9ePXh5eeU5MjZx4kTs378fZ8+elSiZZnXr1k3t61evXuHSpUtITk5GmzZtsHXrVomSFYwFhYgAAIGBgZg/fz5GjBgBT09PAK+PJixduhSjR4/GjBkzJE6oORs2bMCKFSsQExODyMhIVK1aFQsXLoSTkxO6dOkidTwqRsbGxrh48SKqV6+utvzGjRuoXbs2MjIyJEpW8nJycjB06FB88sknGD9+vNRx8sh7vRUR6aTly5fjl19+QVBQEDp37ozOnTsjKCgIq1atws8//yx1PI1Zvnw5/P398cUXXyA5OVl1mamlpSUWLlwobTgqdtbW1oiOjs6zPDo6GjY2NiUfSEJ6enrw9/fHggULpI6SL17FQ0QAXh/y9fDwyLO8QYMGyMrKkiBRyViyZAl++eUXdO3aVe2wv4eHB8aOHSthMtKEQYMGYfDgwbhz545qtujjx4/j3//+N/z9/SVOV/Ju374t279vFhQiAgD07dsXy5cvx/z589WWr1q1Cj4+PhKl0ryYmBjUq1cvz3IjIyOkpaVJkIg0acqUKTA3N8e8efNU0wbY29tj2rRpWnspPYA85UsIgYcPH2LXrl3w9fWVKNW7saAQ6bA3X7QUCgVWr16N/fv3q6a4j4qKQlxcHPr16ydVRI1zcnJCdHR0nvkx9u7dC1dXV4lSkaYoFAqMHj0ao0ePxrNnzwC8vk+Ntjt37pza13p6erC2tsa8efPee4WPVFhQiHTY2y9aDRo0APD6sC8AVKhQARUqVMDly5dLPFtJ8ff3h5+fHzIyMiCEwMmTJ/Hbb78hKCgIq1evljoeaZAuFJNcu3btghBCNQFj7v2mqlatCn19eVYBXsVDRDpv48aNmDZtmqqY2dvbY/r06Rg4cKDEyag41K9fH2FhYShXrhzq1av3zvl9tPUy4/bt26N79+4YMmQIkpOT4eLiAgMDAzx+/Bjz58/H0KFDpY6YhzxrExFRCcjKykJoaCi8vLzg4+OD9PR0PH/+XOeu5tB2Xbp0Uc0E3aVLF52cgPDs2bOqq3W2bNkCW1tbnDt3Dn/++ScCAwNlWVB4BIWIdJqpqSmuXr2qs/doId1gamqKa9euwcHBAV999RVq1qyJqVOnIj4+HjVq1EB6errUEfPgPChEpNMaNWqUZywOaS9nZ2c8efIkz/Lk5GQ4OztLkKhkVKtWDdu2bUN8fDz27duH9u3bAwCSkpKgVColTpc/nuIhIp02bNgwjBkzBvfu3cv3Ls61a9eWKBlpwt27d1WT8b0pMzMT9+7dkyBRyQgMDESfPn0wevRotG3bVjVb9P79+/O9zF4OeIqHiHSanl7eA8kKhQJCCCgUinzfzKj02b59OwCga9euWL9+PSwsLFTrsrOzERYWhgMHDuD69etSRdS4hIQEPHz4EHXq1FH93p88eRJKpRIuLi4Sp8uLBYWIdFpsbOw713NsinbIfUPOLZ9vMjAwgKOjI+bNm4cvv/xSiniUD57iISKdFhoaCltb2zyTVa1duxaPHj3ChAkTJEpGxSknJwfA64n5Tp06hQoVKkiciN6Hg2SJSKetXLky38PbNWvWxIoVKyRIRJoUExPDclJK8AgKEem0hIQEVKxYMc9ya2trPHz4UIJEpGlpaWkIDw9HXFwcXr58qbZOm+/HU9qwoBCRTqtSpQqOHz8OJycnteXHjx+Hvb29RKlIU86dO4cvvvgC6enpSEtLg5WVFR4/fgxTU1PY2NiwoMgIT/EQkU4bNGgQRo0ahXXr1iE2NhaxsbFYu3YtRo8ejUGDBkkdj4rZ6NGj0alTJ/zzzz8wMTHBiRMnEBsbiwYNGuCnn36SOh69gVfxEJFOE0Jg4sSJWLx4sepwv7GxMSZMmIDAwECJ01Fxs7S0RFRUFGrUqAFLS0tERkbC1dUVUVFR8PX1xbVr16SOSP+Hp3iISKcpFAr8+9//xpQpU3D16lWYmJigevXqqnu3kHYxMDBQXXJsY2ODuLg4uLq6wsLCAvHx8RKnozexoBARAShbtiwaNmwodQzSsHr16uHUqVOoXr06WrZsicDAQDx+/BgbNmxArVq1pI5Hb+ApHiIi0hmnT5/Gs2fP0Lp1ayQlJaFfv36IiIhA9erVsXbtWtSpU0fqiPR/WFCIiIhIdngVDxEREckOx6AQEZHOePLkCQIDA3H48GEkJSWppsDP9fTpU4mS0dtYUIiISGf07dsXt27dwsCBA2FrawuFQiF1JCoAx6AQEZHOMDc3x7FjxzgYthTgGBQiItIZLi4uePHihdQxqBB4BIWIiHTGqVOnMHHiRAQGBqJWrVowMDBQW69UKiVKRm/jGBQiItIZlpaWSE1NRZs2bdSWCyGgUCiQnZ0tUTJ6GwsKERHpDB8fHxgYGCA0NJSDZGWOp3iIiEhnmJqa4ty5c6hRo4bUUeg9OEiWiIh0hoeHB28KWErwCAoREemMP/74A9OmTcO4cePg7u6eZ5Bs7dq1JUpGb2NBISIinaGnl/fEgUKh4CBZGeIgWSIi0hkxMTFSR6BC4hEUIiLSCa9evYKLiwt27twJV1dXqePQe3CQLBER6QQDAwNkZGRIHYMKiQWFiIh0hp+fH/79738jKytL6ij0HjzFQ0REOqNbt24ICwtD2bJl4e7uDjMzM7X1W7dulSgZvY2DZImISGdYWlqiR48eUsegQuARFCIiIpIdHkEhIiKd8+jRI1y/fh0AUKNGDVhbW0uciN7GQbJERKQz0tLS8O2336JixYpo0aIFWrRoAXt7ewwcOBDp6elSx6M3sKAQEZHO8Pf3R3h4OHbs2IHk5GQkJyfj77//Rnh4OMaMGSN1PHoDx6AQEZHOqFChArZs2YJWrVqpLT98+DC++uorPHr0SJpglAePoBARkc5IT0+Hra1tnuU2NjY8xSMzPIJCREQ6o23btihfvjx+/fVXGBsbAwBevHgBX19fPH36FAcPHpQ4IeViQSEiIp1x8eJFdOjQAZmZmahTpw4A4Pz58zA2Nsa+fftQs2ZNiRNSLhYUIiLSKenp6di4cSOuXbsGAHB1dYWPjw9MTEwkTkZvYkEhIiKtVr9+fYSFhaFcuXKYMWMGxo4dC1NTU6lj0XuwoBARkVYzMTHBzZs3UblyZZQpUwYPHz6EjY2N1LHoPTiTLBERabW6detiwIABaN68OYQQ+Omnn1C2bNl8tw0MDCzhdFQQHkEhIiKtdv36dUydOhW3b9/G2bNn4ebmBn39vJ/PFQoFzp49K0FCyg8LChER6Qw9PT0kJCTwFE8pwIJCREREssMxKEREpFNu3ryJw4cPIykpCTk5OWrrOAZFPngEhYiIdMYvv/yCoUOHokKFCrCzs4NCoVCt4xgUeWFBISIinVG1alUMGzYMEyZMkDoKvQcLChER6QylUono6Gg4OztLHYXeg3czJiIindGzZ0/s379f6hhUCBwkS0REOqNatWqYMmUKTpw4AXd3dxgYGKit/+GHHyRKRm/jKR4iItIZTk5OBa5TKBS4c+dOCaahd2FBISIiItnhKR4iItJq/v7+mDlzJszMzODv71/gdgqFAvPmzSvBZPQuLChERKTVzp07h1evXqn+uyBvzolC0uMpHiIiIpIdXmZMREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLDgkJERESyw4JCREREssOCQkRERLLz/wCzNsj7Qdq9UAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.Series(dolly['category']).value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ad76873",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "0ad76873",
    "outputId": "db541b72-19d9-4a94-be62-a02d384f645e"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
       "        features: ['instruction', 'context', 'response', 'category', 'input_ids', 'attention_mask', 'input_text', 'formatted_convo'],\n",
       "        num_rows: 1800\n",
       "    })\n",
       "    test: Dataset({\n",
       "        features: ['instruction', 'context', 'response', 'category', 'input_ids', 'attention_mask', 'input_text', 'formatted_convo'],\n",
       "        num_rows: 200\n",
       "    })\n",
       "})"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dolly = dolly.train_test_split(test_size=0.1, seed=42)\n",
    "\n",
    "dolly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d29c751",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "4d29c751",
    "outputId": "7882a99b-23b3-4652-8eb4-973db2fca6d4",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'instruction': 'Why did the earliest vendors and retailers in the U.S. decide to charge 99 cents rather than a dollar?',\n",
       " 'context': '',\n",
       " 'response': 'Because the cashier would have to make change in the cash register for 99 cents, forcing a record of the sale, whereas if the vendor/retailer charged $1, the cashier could pocket the dollar without a record of the transaction.',\n",
       " 'category': 'open_qa',\n",
       " 'input_ids': tensor([[128257,   8595,   1550,    279,  30758,  29629,    323,  30282,    304,\n",
       "             279,    549,    815,     13,  10491,    311,   6900,    220,   1484,\n",
       "           31291,   4856,   1109,    264,  18160,     30,    220, 128258]]),\n",
       " 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "          1, 1]]),\n",
       " 'input_text': '###HUMAN### Why did the earliest vendors and retailers in the U.S. decide to charge 99 cents rather than a dollar? ###BOT###',\n",
       " 'formatted_convo': '###HUMAN### Why did the earliest vendors and retailers in the U.S. decide to charge 99 cents rather than a dollar? ###BOT###'}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dolly['train'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1kttZQsHworz",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "id": "1kttZQsHworz",
    "outputId": "4d52e685-3453-4bae-df1c-7263813aad0a"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'###HUMAN### Why did the earliest vendors and retailers in the U.S. decide to charge 99 cents rather than a dollar? ###BOT### While the exact reasons are unknown, some speculate the $0.99 may have been chosen as a way to set prices lower than competitors to increase sales. Another'"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generation_kwargs = {\n",
    "    \"do_sample\": True,\n",
    "    \"eos_token_id\": EXTRA_TOKENS['stop_token']['token_id'],\n",
    "    'pad_token_id': EXTRA_TOKENS['stop_token']['token_id']\n",
    "}\n",
    "\n",
    "output = model.generate(\n",
    "    **tokenizer(\n",
    "        dolly['train'][0]['formatted_convo'],\n",
    "        return_tensors='pt', max_length=128, truncation=True\n",
    "        ).to(0),\n",
    "    **generation_kwargs,\n",
    "    max_new_tokens=32\n",
    ")\n",
    "tokenizer.decode(output[0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "YIe5ORBHnd62",
   "metadata": {
    "id": "YIe5ORBHnd62"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1IvI1w6ujXo1",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "1IvI1w6ujXo1",
    "outputId": "3cfc8c7e-a814-4c51-d324-4c50a49404eb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Parameter containing:\n",
       "tensor([[ 0.0011, -0.0118, -0.0147,  ...,  0.0131,  0.0011, -0.0015]],\n",
       "       device='cuda:0', requires_grad=True)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.v_head.summary.weight"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "oTYMagonVmwh",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "oTYMagonVmwh",
    "outputId": "986b576b-5d2d-4fd3-92ef-02942d5869d0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "v_head.summary.weight is trainable with 4096 params\n",
      "v_head.summary.bias is trainable with 1 params\n",
      "trainable params: 4097 || all params: 4595154945 || trainable%: 8.915912627620873e-05\n"
     ]
    }
   ],
   "source": [
    "def print_trainable_parameters(model, verbose=False):\n",
    "    \"\"\"\n",
    "    Prints the number of trainable parameters in the model.\n",
    "    \"\"\"\n",
    "    trainable_params = 0\n",
    "    all_param = 0\n",
    "    for _, param in model.named_parameters():\n",
    "        all_param += param.numel()\n",
    "        if param.requires_grad:\n",
    "            if verbose:\n",
    "                print(f'{_} is trainable with {param.numel()} params')\n",
    "            trainable_params += param.numel()\n",
    "    print(\n",
    "        f\"trainable params: {trainable_params} || all params: {all_param} || trainable%: {100 * trainable_params / all_param}\"\n",
    "    )\n",
    "\n",
    "print_trainable_parameters(model, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "djyL6C0dmiBB",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "djyL6C0dmiBB",
    "outputId": "9390c17c-486a-4214-d80a-73117a87f8f9"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['_enable_peft_forward_hooks']"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(filter(lambda x: 'enable' in x, dir(model.pretrained_model)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "IMpCis5qKKKC",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "IMpCis5qKKKC",
    "outputId": "3d0e890d-581a-404e-c2fb-f896ced6a595"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "Turning ON: pretrained_model.base_model.model.model.layers.31.self_attn.v_proj.lora_A.default.weight\n",
      "Turning ON: pretrained_model.base_model.model.model.layers.31.self_attn.v_proj.lora_B.default.weight\n"
     ]
    }
   ],
   "source": [
    "for name, param in model.named_parameters():\n",
    "    if 'lora' in name and not param.requires_grad:\n",
    "        print(f'Turning ON: {name}')\n",
    "        param.requires_grad = True\n",
    "    elif 'original_module' in name and param.requires_grad:\n",
    "        print(f'Turning OFF: {name}')\n",
    "        param.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea42b668",
   "metadata": {
    "id": "ea42b668",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "import wandb\n",
    "\n",
    "def collator(data):\n",
    "    return dict((key, [d[key] for d in data]) for key in data[0])\n",
    "\n",
    "# Set up Weights and Biases integration\n",
    "# wandb.init(project=\"odsc-sawyer-rlhf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Eq_IvX6Dmp3y",
   "metadata": {
    "id": "Eq_IvX6Dmp3y"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e625c5df",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "e625c5df",
    "outputId": "f6860edd-2edb-4b55-8929-a1e3c439d96a",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(tensor([[128257,   8595,   1550,    279,  30758,  29629,    323,  30282,    304,\n",
       "             279,    549,    815,     13,  10491,    311,   6900,    220,   1484,\n",
       "           31291,   4856,   1109,    264,  18160,     30,    220, 128258]]),\n",
       " ['###HUMAN### Why did the earliest vendors and retailers in the U.S. decide to charge 99 cents rather than a dollar? ###BOT###'])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dolly['train'][0]['input_ids'], tokenizer.batch_decode(dolly['train'][0]['input_ids'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "E3PnpErMjBws",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "E3PnpErMjBws",
    "outputId": "0bdee390-d91f-4b97-a1d9-83e8b86a0bf7"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/transformers/generation/configuration_utils.py:427: UserWarning: `do_sample` is set to `False`. However, `top_k` is set to `0.0` -- this flag is only used in sample-based generation modes. You should set `do_sample=True` or unset `top_k`.\n",
      "  warnings.warn(\n",
      "Setting `pad_token_id` to `eos_token_id`:128256 for open-end generation.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['###HUMAN### Why did the earliest vendors and retailers in the U.S. decide to charge 99 cents rather than a dollar? ###BOT### The reason for this is that the price of 99 cents is more appealing to customers than the price of a dollar. It is a psychological trick that makes the product seem cheaper than it actually is. ###STOP###']"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenizer.batch_decode(model.generate(\n",
    "    **tokenizer(\n",
    "        dolly['train'][0]['formatted_convo'],\n",
    "        return_tensors='pt',\n",
    "    ).to(0),\n",
    "    eos_token_id=EXTRA_TOKENS['stop_token']['token_id'],\n",
    "    max_new_tokens=128,\n",
    "    min_new_tokens=2,\n",
    "    top_k=0.0,\n",
    "    top_p=1.0,\n",
    "    ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "nHj2y84ujCcZ",
   "metadata": {
    "id": "nHj2y84ujCcZ"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Ax4S91eRjPRY",
   "metadata": {
    "id": "Ax4S91eRjPRY"
   },
   "outputs": [],
   "source": []
  },
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     "data": {
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    {
     "name": "stderr",
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     "text": [
      "Some weights of the model checkpoint at textattack/roberta-base-CoLA were not used when initializing RobertaForSequenceClassification: ['roberta.pooler.dense.bias', 'roberta.pooler.dense.weight']\n",
      "- This IS expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n",
      "- This IS NOT expected if you are initializing RobertaForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n"
     ]
    }
   ],
   "source": [
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
    "\n",
    "roberta_tokenizer = AutoTokenizer.from_pretrained(\"textattack/roberta-base-CoLA\")\n",
    "roberta_model = AutoModelForSequenceClassification.from_pretrained(\"textattack/roberta-base-CoLA\")\n",
    "\n",
    "cola_pipeline = pipeline('text-classification', model=roberta_model, tokenizer=roberta_tokenizer)\n",
    "\n",
    "def get_cola_scores(texts):\n",
    "    scores = []\n",
    "    # note function_to_apply='none' gives me logits which can be negative (what we want)\n",
    "    results = cola_pipeline(texts, function_to_apply='none', top_k=None)\n",
    "    for result in results:\n",
    "        for label in result:\n",
    "            if label['label'] == 'LABEL_1':  # good grammar:\n",
    "                scores.append(label['score'])\n",
    "    return scores\n"
   ]
  },
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      "text/plain": [
       "1_Pooling/config.json:   0%|          | 0.00/190 [00:00<?, ?B/s]"
      ]
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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.4387131631374359, 0.14182493090629578]\n"
     ]
    }
   ],
   "source": [
    "from sentence_transformers import SentenceTransformer, util\n",
    "\n",
    "sim_model = SentenceTransformer(\"nq-distilbert-base-v1\")\n",
    "\n",
    "def get_similarity_scores(queries, responses):\n",
    "    \"\"\"\n",
    "    Calculate similarity scores for each pair of sentences from queries and responses.\n",
    "\n",
    "    Args:\n",
    "        queries (list): List of query sentences.\n",
    "        responses (list): List of response sentences.\n",
    "\n",
    "    Returns:\n",
    "        list: List containing similarity scores for each pair.\n",
    "    \"\"\"\n",
    "\n",
    "    # Encode the queries and responses\n",
    "    query_embeddings = sim_model.encode(queries)\n",
    "    response_embeddings = sim_model.encode(responses)\n",
    "\n",
    "    # Calculate similarity scores for each pair\n",
    "    scores = []\n",
    "    for q_embed, r_embed in zip(query_embeddings, response_embeddings):\n",
    "        similarity = util.cos_sim(q_embed, r_embed)\n",
    "        scores.append(similarity.item())\n",
    "\n",
    "    return scores\n",
    "\n",
    "print(get_similarity_scores(['how do you greet someone?', 'how do you greet someone?'], ['tell them hello', 'tell them bye']))"
   ]
  },
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   ],
   "source": [
    "reward_model_name = 'profoz/sawyer-llama-reward' # From our rewards notebook\n",
    "\n",
    "reward_pipe = pipeline(\"text-classification\", model=reward_model_name)\n",
    "\n",
    "def get_reward_scores(queries, responses):\n",
    "    scores = []\n",
    "    inputs = [{'text': q, 'text_pair': r} for q, r in zip(queries, responses)]\n",
    "    responses = reward_pipe(\n",
    "        inputs,\n",
    "        function_to_apply='none',\n",
    "        truncation=True,\n",
    "        max_length=511\n",
    "    )\n",
    "    return [_['score'] for _ in responses]\n"
   ]
  },
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   "execution_count": null,
   "id": "SKTz-QNW6oJI",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "SKTz-QNW6oJI",
    "outputId": "69bf826a-2d45-4d15-c9bb-cae79b137de4"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.4301540851593018, -0.219132661819458]\n",
      "[-6.883139610290527, -6.443085670471191]\n",
      "[1.0, 0.9999998807907104]\n"
     ]
    }
   ],
   "source": [
    "print(get_cola_scores(['test', 'hi']))\n",
    "print(get_reward_scores(['test', 'hi'], ['test', 'hi']))\n",
    "print(get_similarity_scores(['test', 'hi'], ['test', 'hi']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "lpQiypfkdQLD",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "lpQiypfkdQLD",
    "outputId": "d495df95-7e8e-4231-a137-611ecccd686a"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'do_sample': True, 'eos_token_id': 128256, 'pad_token_id': 128256}"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "generation_kwargs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "JHDBxz8EnImz",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "JHDBxz8EnImz",
    "outputId": "be9f52ea-c043-49e7-fa5b-3183acbcbc6a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[-6.883139610290527, -6.443085670471191]\n"
     ]
    }
   ],
   "source": [
    "print(get_reward_scores(['test', 'hi'], ['test', 'hi']))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "nTXHzfDkNk-0",
   "metadata": {
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   },
   "outputs": [],
   "source": []
  },
  {
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   "execution_count": null,
   "id": "y3tHFF3bfQMu",
   "metadata": {
    "id": "y3tHFF3bfQMu"
   },
   "outputs": [],
   "source": [
    "ref_model = create_reference_model(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cy1y9nScy9D6",
   "metadata": {
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    },
    "id": "cy1y9nScy9D6",
    "outputId": "491cb56d-1aee-4903-8e5b-7bbc01c62db7"
   },
   "outputs": [
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       " View run at <a href='https://wandb.ai/profoz/trl/runs/jcch7fzx' target=\"_blank\">https://wandb.ai/profoz/trl/runs/jcch7fzx</a>"
      ],
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       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
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   ],
   "source": [
    "ppo_config = PPOConfig(\n",
    "    model_name='sawyer_rl',\n",
    "    learning_rate=1.41e-5,\n",
    "    batch_size=8,\n",
    "    gradient_accumulation_steps=4,\n",
    "    ppo_epochs=2,\n",
    "    seed=42,\n",
    "    log_with=\"wandb\",\n",
    "    optimize_cuda_cache=True,\n",
    "    early_stopping=True\n",
    ")\n",
    "\n",
    "ppo_trainer = PPOTrainer(\n",
    "    ppo_config,\n",
    "    model,\n",
    "    ref_model=ref_model,\n",
    "    tokenizer=tokenizer,\n",
    "    dataset=dolly['train'],\n",
    "    data_collator=collator,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "_MuJ3SCVJDaq",
   "metadata": {
    "id": "_MuJ3SCVJDaq"
   },
   "outputs": [],
   "source": [
    "def make_test_scores(model):\n",
    "    test_scores = {'reward': [], 'cola': [], 'responses': [], 'queries': [], 'similarity': []}\n",
    "    for input_ids in tqdm(dolly['test']['input_ids']):\n",
    "        response = model.generate(\n",
    "            input_ids=input_ids.to(device), **generation_kwargs,\n",
    "            max_new_tokens=256\n",
    "        )\n",
    "        response = tokenizer.decode(response[0])\n",
    "        try:\n",
    "            cleaned_query = response.split(EXTRA_TOKENS['bot_token']['token'])[0].split(EXTRA_TOKENS['human_token']['token'])[1]\n",
    "            cleaned_response = response.split(EXTRA_TOKENS['bot_token']['token'])[1].split(EXTRA_TOKENS['stop_token']['token'])[0]\n",
    "\n",
    "            r = get_reward_scores(\n",
    "                queries=[cleaned_query],\n",
    "                responses=[cleaned_response]\n",
    "            )\n",
    "            c = get_cola_scores(\n",
    "                [cleaned_response]\n",
    "            )\n",
    "            s = get_similarity_scores(\n",
    "                queries=[cleaned_query],\n",
    "                responses=[cleaned_response]\n",
    "            )\n",
    "\n",
    "            test_scores['reward'] += r\n",
    "            test_scores['cola'] +=  c\n",
    "            test_scores['similarity'] += s\n",
    "\n",
    "            test_scores['responses'].append(cleaned_response)\n",
    "            test_scores['queries'].append(cleaned_query)\n",
    "\n",
    "\n",
    "        except Exception as e:\n",
    "            print(f'response: {response}. Error: {e}')\n",
    "\n",
    "    return test_scores\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "qdoR4gx3IRsy",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "qdoR4gx3IRsy",
    "outputId": "07a312eb-b7b8-4493-8ba9-1dae071fa9d8"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'instruction': 'What is the difference between solar power and hydrogen fuel cell?  What are the advantages of hydrogen?',\n",
       " 'context': '',\n",
       " 'response': 'Solar captures the harvesting of the sun while hydrogen is the osmosis process of extracting the hydrogen cells to create energy.  Hydrogen allows for a more consistent energy source while also enabling higher load generation over solar.',\n",
       " 'category': 'general_qa',\n",
       " 'input_ids': tensor([[128257,   3639,    374,    279,   6811,   1990,  13238,   2410,    323,\n",
       "           35784,  10633,   2849,     30,    220,   3639,    527,    279,  22934,\n",
       "             315,  35784,     30,    220, 128258]]),\n",
       " 'attention_mask': tensor([[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]),\n",
       " 'input_text': '###HUMAN### What is the difference between solar power and hydrogen fuel cell?  What are the advantages of hydrogen? ###BOT###',\n",
       " 'formatted_convo': '###HUMAN### What is the difference between solar power and hydrogen fuel cell?  What are the advantages of hydrogen? ###BOT###'}"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dolly['test'][4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "zW2h1m2PK5g5",
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       "  0%|          | 0/200 [00:00<?, ?it/s]"
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     "text": [
      "           reward        cola  similarity\n",
      "count  200.000000  200.000000  200.000000\n",
      "mean     5.702665    1.142447    0.601367\n",
      "std      3.518846    0.485328    0.229995\n",
      "min     -6.876436   -0.726761    0.022396\n",
      "25%      5.586220    0.923370    0.474002\n",
      "50%      7.444443    1.250957    0.656341\n",
      "75%      7.783892    1.481098    0.792025\n",
      "max      7.938239    1.993711    0.961914\n"
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       "      <th>reward</th>\n",
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       "      <th>responses</th>\n",
       "      <th>queries</th>\n",
       "      <th>similarity</th>\n",
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       "      <td>7.111549</td>\n",
       "      <td>0.909536</td>\n",
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       "      <td>7.778332</td>\n",
       "      <td>1.425297</td>\n",
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       "      <td>0.700383</td>\n",
       "      <td>Sure, I can help you plan a first date in San...</td>\n",
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       "      <td>Tortola, Virgin Gorda, Anegada, and Jost van ...</td>\n",
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       "      <td>0.168026</td>\n",
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       "\n",
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       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
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       "    30% {\n",
       "      border-color: transparent;\n",
       "      border-left-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    40% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-top-color: var(--fill-color);\n",
       "    }\n",
       "    60% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "    }\n",
       "    80% {\n",
       "      border-color: transparent;\n",
       "      border-right-color: var(--fill-color);\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "    90% {\n",
       "      border-color: transparent;\n",
       "      border-bottom-color: var(--fill-color);\n",
       "    }\n",
       "  }\n",
       "</style>\n",
       "\n",
       "  <script>\n",
       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-132063bb-399e-4dce-9187-c627535e6bfd button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "     reward      cola                                          responses  \\\n",
       "0  7.111549  0.909536   VCF (Variant Call Format) is a file format us...   \n",
       "1  7.837705  0.586284   When doing meal prep efficiently, there are s...   \n",
       "2  6.141927 -0.112614                             Colorado, Washington.    \n",
       "3  7.865098  1.149171   Yes, many RVs have bathrooms and some types o...   \n",
       "4  7.778332  1.425297   Solar power is a renewable energy that conver...   \n",
       "5  7.921837  1.412548   Southern France and Spain are excellent place...   \n",
       "6  7.908982  1.335448   Rapid economic growth in Libya prior to 1990 ...   \n",
       "7  4.091623  0.700383   Sure, I can help you plan a first date in San...   \n",
       "8  7.572845  1.339877   According to Wikipedia, the Summer Breeze Ope...   \n",
       "9  6.162791  0.980713   Tortola, Virgin Gorda, Anegada, and Jost van ...   \n",
       "\n",
       "                                             queries  similarity  \n",
       "0                                      What is VCF?     0.446424  \n",
       "1   How do you do meal prep efficiently and make ...    0.814939  \n",
       "2   Tell me which of these States' shapes have ex...    0.208697  \n",
       "3                            Do RVs have bathrooms?     0.830622  \n",
       "4   What is the difference between solar power an...    0.814308  \n",
       "5   What is the best location for a family vacati...    0.725205  \n",
       "6   What enabled Libya to obtain rapid economic g...    0.700436  \n",
       "7           Help plan a first date in San Francisco     0.528415  \n",
       "8   How many people attended the Summer Breeze Op...    0.547274  \n",
       "9   What are the four largest British Virgin Isla...    0.168026  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "old_scores = make_test_scores(model)\n",
    "print(pd.DataFrame(old_scores).describe())\n",
    "\n",
    "pd.DataFrame(old_scores).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "D2-TDpyggd-L",
   "metadata": {
    "id": "D2-TDpyggd-L"
   },
   "outputs": [],
   "source": [
    "torch.cuda.empty_cache()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "S4RLkKESRjqJ",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "S4RLkKESRjqJ",
    "outputId": "20bace22-7bbe-4cb0-8b0b-2765c0a19bbb"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(227, 4)"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "class LengthSampler:\n",
    "    def __init__(self, min_value, max_value):\n",
    "        self.values = list(range(min_value, max_value))\n",
    "    def __call__(self):\n",
    "        return int(np.random.choice(self.values))\n",
    "\n",
    "max_output_size = LengthSampler(128, 256)\n",
    "min_output_size = LengthSampler(2, 8)\n",
    "\n",
    "max_output_size(), min_output_size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ta-Um_etoDms",
   "metadata": {
    "id": "ta-Um_etoDms"
   },
   "outputs": [],
   "source": [
    "def combine_reward_and_sim(r, s):\n",
    "    if s < 0 and r < 0:\n",
    "        return -(r * s)\n",
    "    else:\n",
    "        return r * s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "VYlQLxsI9FA9",
   "metadata": {
    "id": "VYlQLxsI9FA9"
   },
   "outputs": [],
   "source": [
    "# Future work: replace with another LLM supervising, maybe even sawyer itself to use as red teaming?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ePH5OvYPiunD",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 35
    },
    "id": "ePH5OvYPiunD",
    "outputId": "38c3ec90-5a0a-4f58-e86c-1341e4f9e73e"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'###STOP###'"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "EXTRA_TOKENS['stop_token']['token']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f7546817",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 113,
     "referenced_widgets": [
      "58e9c4547bbf4e8ca89be4dd0d663c30",
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    },
    "id": "f7546817",
    "outputId": "ec0ec044-2115-4ed3-dfda-48f030da3d80",
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "58e9c4547bbf4e8ca89be4dd0d663c30",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/2 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c0eb7ebbe3054d75927f534bd3414b05",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/225 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "6155dcd75e494beda8d9cf8c2dd35b89",
       "version_major": 2,
       "version_minor": 0
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      "text/plain": [
       "  0%|          | 0/225 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "steps = 0\n",
    "rlhf_repo_name = 'sawyer-llama-3-rlf'\n",
    "QUERY_KEY = EXTRA_TOKENS['human_token']['token']\n",
    "RESPONSE_KEY = EXTRA_TOKENS['bot_token']['token']\n",
    "\n",
    "for epoch in tqdm(range(ppo_config.ppo_epochs)):\n",
    "    ppo_trainer.dataset = ppo_trainer.dataset.shuffle() # shuffle every epoch!\n",
    "    for batch in tqdm(ppo_trainer.dataloader):\n",
    "        batch['response'] = []\n",
    "        batch['query'] = []\n",
    "        batch['rewards'] = []\n",
    "        response_tensors = []\n",
    "        for input_ids in batch[\"input_ids\"]:\n",
    "            generation_kwargs.update({'max_new_tokens': max_output_size()})\n",
    "            generation_kwargs.update({'min_new_tokens': min_output_size()})\n",
    "            batch['query'].append(\n",
    "                tokenizer.batch_decode(input_ids, skip_special_tokens=False)[0].split(QUERY_KEY)[1].split(RESPONSE_KEY)[0].strip())\n",
    "            response_tensor = ppo_trainer.generate(\n",
    "                input_ids.squeeze(), return_prompt=False, **generation_kwargs,\n",
    "            )\n",
    "            batch['response'].append(tokenizer.batch_decode(response_tensor, skip_special_tokens=True)[0].replace(EXTRA_TOKENS['stop_token']['token'], ''))\n",
    "            response_tensors.append(response_tensor.squeeze())\n",
    "        # Run PPO step\n",
    "        try:\n",
    "            batch['reward_score'] = get_reward_scores(batch['query'], batch['response'])\n",
    "            # ONLY USING our reward mechanism here\n",
    "\n",
    "            # uncomment this and the line below to use all three\n",
    "            # batch['cola_score'] = get_cola_scores(batch['response'])\n",
    "            # batch['similarity_score'] = get_similarity_scores(batch['query'], batch['response'])\n",
    "        except Exception as e:\n",
    "            print('Skipping batch', e)\n",
    "            print(batch)\n",
    "            continue\n",
    "\n",
    "        batch['rewards'] = [torch.tensor(r) for r in batch['reward_score']]\n",
    "        # batch['rewards'] = [torch.tensor(combine_reward_and_sim(r, s) + 0.5 * c) for r, c, s in zip(batch['reward_score'], batch['cola_score'], batch['similarity_score'])]\n",
    "\n",
    "        stats = ppo_trainer.step([_.squeeze() for _ in batch[\"input_ids\"]], response_tensors, batch['rewards'])\n",
    "        ppo_trainer.log_stats(stats, batch, batch['rewards'])\n",
    "\n",
    "        steps += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "VXlb4mi2y-io",
   "metadata": {
    "id": "VXlb4mi2y-io"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "pDXe9ascr1lF",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 332,
     "referenced_widgets": [
      "d5ce596de54041728bca2b51c358cf3e",
      "7259e8aad04a4a8c9a3b29463ef80dd6",
      "7f1c8efc88de4cd1945bb10276930651",
      "40ab48c0998647ff8e545010032b627b",
      "23ef6ca1665b4403a9fe52dc5a1a5ce1",
      "d8e2d573909b482dbe6bcf246c6131d6",
      "2e0668952ffb4975b78ce1abf403f8a8",
      "e03b1093e2e14e8db8b666a0a5a57c30",
      "fa98fd232789460ea08ea13af1f1d7ac",
      "bc0d56d078ae4dad8792638cbb15838a",
      "7c99c234d68c4310ba1c7d92c07b8c17"
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    },
    "id": "pDXe9ascr1lF",
    "outputId": "c2cef33e-a706-4460-f7a0-397325ff0a0e"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d5ce596de54041728bca2b51c358cf3e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "  0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"reward\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 69.12496471241086,\n        \"min\": -4.915857315063477,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          6.924859007038176,\n          7.597067594528198,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"cola\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 70.42816176428319,\n        \"min\": -1.1883686780929565,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.0497603712044656,\n          1.1417970657348633,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"similarity\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 70.52807752555096,\n        \"min\": 0.15844853222370148,\n        \"max\": 200.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5879347220063209,\n          0.5986984074115753,\n          200.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
       "type": "dataframe"
      },
      "text/html": [
       "\n",
       "  <div id=\"df-fb99ada6-a3a3-40b1-84ad-99f4f916cc28\" class=\"colab-df-container\">\n",
       "    <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>reward</th>\n",
       "      <th>cola</th>\n",
       "      <th>similarity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "      <td>200.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>6.924859</td>\n",
       "      <td>1.049760</td>\n",
       "      <td>0.587935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.858197</td>\n",
       "      <td>0.556134</td>\n",
       "      <td>0.168621</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>-4.915857</td>\n",
       "      <td>-1.188369</td>\n",
       "      <td>0.158449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.059830</td>\n",
       "      <td>0.771734</td>\n",
       "      <td>0.470924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.597068</td>\n",
       "      <td>1.141797</td>\n",
       "      <td>0.598698</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>7.799540</td>\n",
       "      <td>1.462207</td>\n",
       "      <td>0.718332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.922405</td>\n",
       "      <td>1.918363</td>\n",
       "      <td>0.921735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
       "    <div class=\"colab-df-buttons\">\n",
       "\n",
       "  <div class=\"colab-df-container\">\n",
       "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-fb99ada6-a3a3-40b1-84ad-99f4f916cc28')\"\n",
       "            title=\"Convert this dataframe to an interactive table.\"\n",
       "            style=\"display:none;\">\n",
       "\n",
       "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\" viewBox=\"0 -960 960 960\">\n",
       "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
       "  </svg>\n",
       "    </button>\n",
       "\n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
       "      background-color: #E8F0FE;\n",
       "      border: none;\n",
       "      border-radius: 50%;\n",
       "      cursor: pointer;\n",
       "      display: none;\n",
       "      fill: #1967D2;\n",
       "      height: 32px;\n",
       "      padding: 0 0 0 0;\n",
       "      width: 32px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "      fill: #174EA6;\n",
       "    }\n",
       "\n",
       "    .colab-df-buttons div {\n",
       "      margin-bottom: 4px;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
       "      fill: #D2E3FC;\n",
       "    }\n",
       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "    <script>\n",
       "      const buttonEl =\n",
       "        document.querySelector('#df-fb99ada6-a3a3-40b1-84ad-99f4f916cc28 button.colab-df-convert');\n",
       "      buttonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "      async function convertToInteractive(key) {\n",
       "        const element = document.querySelector('#df-fb99ada6-a3a3-40b1-84ad-99f4f916cc28');\n",
       "        const dataTable =\n",
       "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                    [key], {});\n",
       "        if (!dataTable) return;\n",
       "\n",
       "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "          + ' to learn more about interactive tables.';\n",
       "        element.innerHTML = '';\n",
       "        dataTable['output_type'] = 'display_data';\n",
       "        await google.colab.output.renderOutput(dataTable, element);\n",
       "        const docLink = document.createElement('div');\n",
       "        docLink.innerHTML = docLinkHtml;\n",
       "        element.appendChild(docLink);\n",
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       "\n",
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       "    </g>\n",
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       "  </button>\n",
       "\n",
       "<style>\n",
       "  .colab-df-quickchart {\n",
       "      --bg-color: #E8F0FE;\n",
       "      --fill-color: #1967D2;\n",
       "      --hover-bg-color: #E2EBFA;\n",
       "      --hover-fill-color: #174EA6;\n",
       "      --disabled-fill-color: #AAA;\n",
       "      --disabled-bg-color: #DDD;\n",
       "  }\n",
       "\n",
       "  [theme=dark] .colab-df-quickchart {\n",
       "      --bg-color: #3B4455;\n",
       "      --fill-color: #D2E3FC;\n",
       "      --hover-bg-color: #434B5C;\n",
       "      --hover-fill-color: #FFFFFF;\n",
       "      --disabled-bg-color: #3B4455;\n",
       "      --disabled-fill-color: #666;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart {\n",
       "    background-color: var(--bg-color);\n",
       "    border: none;\n",
       "    border-radius: 50%;\n",
       "    cursor: pointer;\n",
       "    display: none;\n",
       "    fill: var(--fill-color);\n",
       "    height: 32px;\n",
       "    padding: 0;\n",
       "    width: 32px;\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart:hover {\n",
       "    background-color: var(--hover-bg-color);\n",
       "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
       "    fill: var(--button-hover-fill-color);\n",
       "  }\n",
       "\n",
       "  .colab-df-quickchart-complete:disabled,\n",
       "  .colab-df-quickchart-complete:disabled:hover {\n",
       "    background-color: var(--disabled-bg-color);\n",
       "    fill: var(--disabled-fill-color);\n",
       "    box-shadow: none;\n",
       "  }\n",
       "\n",
       "  .colab-df-spinner {\n",
       "    border: 2px solid var(--fill-color);\n",
       "    border-color: transparent;\n",
       "    border-bottom-color: var(--fill-color);\n",
       "    animation:\n",
       "      spin 1s steps(1) infinite;\n",
       "  }\n",
       "\n",
       "  @keyframes spin {\n",
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       "      border-bottom-color: var(--fill-color);\n",
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       "      border-bottom-color: var(--fill-color);\n",
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       "  }\n",
       "</style>\n",
       "\n",
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       "    async function quickchart(key) {\n",
       "      const quickchartButtonEl =\n",
       "        document.querySelector('#' + key + ' button');\n",
       "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
       "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
       "      try {\n",
       "        const charts = await google.colab.kernel.invokeFunction(\n",
       "            'suggestCharts', [key], {});\n",
       "      } catch (error) {\n",
       "        console.error('Error during call to suggestCharts:', error);\n",
       "      }\n",
       "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
       "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
       "    }\n",
       "    (() => {\n",
       "      let quickchartButtonEl =\n",
       "        document.querySelector('#df-ed164adf-253d-41b9-b3ec-f667e6779ec5 button');\n",
       "      quickchartButtonEl.style.display =\n",
       "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "    })();\n",
       "  </script>\n",
       "</div>\n",
       "\n",
       "    </div>\n",
       "  </div>\n"
      ],
      "text/plain": [
       "           reward        cola  similarity\n",
       "count  200.000000  200.000000  200.000000\n",
       "mean     6.924859    1.049760    0.587935\n",
       "std      1.858197    0.556134    0.168621\n",
       "min     -4.915857   -1.188369    0.158449\n",
       "25%      7.059830    0.771734    0.470924\n",
       "50%      7.597068    1.141797    0.598698\n",
       "75%      7.799540    1.462207    0.718332\n",
       "max      7.922405    1.918363    0.921735"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "del generation_kwargs['min_new_tokens']\n",
    "del generation_kwargs['max_new_tokens']\n",
    "new_scores = make_test_scores(model)\n",
    "pd.DataFrame(new_scores).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "FbuUFGTex5dg",
   "metadata": {
    "id": "FbuUFGTex5dg"
   },
   "outputs": [],
   "source": [
    "pd.DataFrame(old_scores).describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "DxsuGOaA1h6R",
   "metadata": {
    "id": "DxsuGOaA1h6R"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# Assuming old_scores and new_scores are defined\n",
    "# Convert the score lists to DataFrames\n",
    "old_scores_df = pd.DataFrame(old_scores, columns=['reward'])\n",
    "new_scores_df = pd.DataFrame(new_scores, columns=['reward'])\n",
    "\n",
    "# Get descriptive statistics\n",
    "old_score_desc = old_scores_df.describe()\n",
    "new_score_desc = new_scores_df.describe()\n",
    "\n",
    "# Statistics to plot\n",
    "stats = ['mean', '50%', 'min', 'max', 'std']\n",
    "labels = ['Mean', 'Median', 'Min', 'Max', 'Std']\n",
    "\n",
    "# Plotting\n",
    "fig, axes = plt.subplots(nrows=1, ncols=2, figsize=(14, 6))\n",
    "\n",
    "# Bar plot for statistics comparison\n",
    "indices = np.arange(len(stats))  # the label locations\n",
    "width = 0.35  # the width of the bars\n",
    "\n",
    "old_values = [old_score_desc.loc[stat, 'reward'] for stat in stats]\n",
    "new_values = [new_score_desc.loc[stat, 'reward'] for stat in stats]\n",
    "\n",
    "axes[0].bar(indices - width/2, old_values, width, label='Old')\n",
    "axes[0].bar(indices + width/2, new_values, width, label='New')\n",
    "\n",
    "axes[0].set_title('Statistics Comparison')\n",
    "axes[0].set_xticks(indices)\n",
    "axes[0].set_xticklabels(labels)\n",
    "axes[0].set_ylabel('Scores')\n",
    "axes[0].legend()\n",
    "\n",
    "# Box plot for distribution comparison\n",
    "combined_scores = pd.concat([old_scores_df.assign(Score_Type='Old'), new_scores_df.assign(Score_Type='New')])\n",
    "axes[1].boxplot([combined_scores[combined_scores['Score_Type']=='Old']['reward'], combined_scores[combined_scores['Score_Type']=='New']['reward']], labels=['Old', 'New'])\n",
    "axes[1].set_title('Score Distribution Comparison')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "V6UbWLQKArmm",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 315,
     "referenced_widgets": [
      "f765f5846abc4aa1a586e932cb05c987",
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      "e4b32a0a2b4a4ed48a1ad7b2e94bd127",
      "33fda207ab374027a625f7475668cffc",
      "3a1cd46f7d7641ff92f37a7ae6e451d4",
      "2d03d95eff4c4d219722bac312177fd3",
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      "01cda650cc87447580fcb0ba1568b145",
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      "4a97544800064826af76631bac6e71dd",
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      "e0c74f32331f48dfaff72ca38281c0b7",
      "4e09af20434b4d0ebd536f2317fbbeff",
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      "741ef1e555ef4ea2816c3085205321c5"
     ]
    },
    "id": "V6UbWLQKArmm",
    "outputId": "17aece87-1ff0-4678-dafd-0d0bfe44d62a"
   },
   "outputs": [
    {
     "metadata": {
      "tags": null
     },
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro-Wilk test for pre-training predictions: statistic=0.6589620113372803, p-value=7.759584870964471e-20\n",
      "Shapiro-Wilk test for post-training predictions: statistic=0.5343424081802368, p-value=8.553391603370727e-23\n",
      "At least one of the datasets is not normally distributed. Proceeding with a non-parametric test.\n",
      "Mann-Whitney U test: statistic=16898.0, p-value=0.007304649832296909\n",
      "----\n",
      "Wilcoxon signed-rank test: statistic=6572.0, p-value=2.198133736688368e-05\n",
      "----\n",
      "The data is significantly different\n",
      "----\n"
     ]
    },
    {
     "metadata": {
      "tags": null
     },
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/peft/utils/save_and_load.py:168: UserWarning: Setting `save_embedding_layers` to `True` as the embedding layer has been resized during finetuning.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f765f5846abc4aa1a586e932cb05c987",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "adapter_model.safetensors:   0%|          | 0.00/2.32G [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b30d9664bb5f4f358f87a70937fdf534",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "README.md:   0%|          | 0.00/5.17k [00:00<?, ?B/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "metadata": {
      "tags": null
     },
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model and tokenizer pushed to hub\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy import stats\n",
    "\n",
    "# Convert the scores to numpy arrays\n",
    "pre_train_predictions = np.array(old_scores['reward'])\n",
    "post_train_predictions = np.array(new_scores['reward'])\n",
    "\n",
    "# Test for normality\n",
    "shapiro_pre = stats.shapiro(pre_train_predictions)\n",
    "shapiro_post = stats.shapiro(post_train_predictions)\n",
    "\n",
    "print(f\"Shapiro-Wilk test for pre-training predictions: statistic={shapiro_pre[0]}, p-value={shapiro_pre[1]}\")\n",
    "print(f\"Shapiro-Wilk test for post-training predictions: statistic={shapiro_post[0]}, p-value={shapiro_post[1]}\")\n",
    "\n",
    "# If the p-value is less than 0.05, we reject the null hypothesis and conclude that the data is not normally distributed.\n",
    "if shapiro_pre[1] < 0.05 or shapiro_post[1] < 0.05:\n",
    "    print(\"At least one of the datasets is not normally distributed. Proceeding with a non-parametric test.\")\n",
    "\n",
    "    # Run Mann-Whitney U test for unpaired data\n",
    "    mwu_stat, mwu_p = stats.mannwhitneyu(pre_train_predictions, post_train_predictions)\n",
    "    print(f\"Mann-Whitney U test: statistic={mwu_stat}, p-value={mwu_p}\\n----\")\n",
    "\n",
    "    # Run Wilcoxon signed-rank test for paired data (if applicable)\n",
    "    # If the data is paired, replace the above test with the following:\n",
    "    wilcoxon_stat, wilcoxon_p = stats.wilcoxon(pre_train_predictions, post_train_predictions)\n",
    "    print(f\"Wilcoxon signed-rank test: statistic={wilcoxon_stat}, p-value={wilcoxon_p}\\n----\")\n",
    "\n",
    "    # If the p-value is less than 0.05, we reject the null hypothesis and conclude that the data is significantly different.\n",
    "    if mwu_p < 0.05 or wilcoxon_p < 0.05 and (pd.DataFrame(new_scores).describe()['reward']['mean'] > pd.DataFrame(old_scores).describe()['reward']['mean']):\n",
    "        print(\"The data is significantly different\\n----\")\n",
    "        model.push_to_hub(f\"{username}/{rlhf_repo_name}\")\n",
    "        tokenizer.push_to_hub(f\"{username}/{rlhf_repo_name}\")\n",
    "        print(\"Model and tokenizer pushed to hub\")\n",
    "    else:\n",
    "        print(\"The data is not significantly different.\")\n",
    "else:\n",
    "    print(\"Both datasets are normally distributed. You may consider using a parametric test like the independent t-test.\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "Fkaj0IUuZaxE",
   "metadata": {
    "colab": {
     "background_save": true
    },
    "id": "Fkaj0IUuZaxE"
   },
   "outputs": [],
   "source": [
    "model.push_to_hub(f\"{username}/{rlhf_repo_name}\")\n",
    "tokenizer.push_to_hub(f\"{username}/{rlhf_repo_name}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "TahJGze2FG3x",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 573
    },
    "id": "TahJGze2FG3x",
    "outputId": "6a594643-f1b9-475c-8cb9-f49024f22c9f"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Assuming you have two DataFrames: new_scores_df and old_scores_df\n",
    "# Add a 'Type' column to each DataFrame to label the data\n",
    "new_scores_df['Type'] = 'New'\n",
    "old_scores_df['Type'] = 'Old'\n",
    "\n",
    "# Concatenate the two DataFrames\n",
    "combined_data = pd.concat([old_scores_df[['reward', 'Type']], new_scores_df[['reward', 'Type']], ])\n",
    "\n",
    "# Create the boxplot\n",
    "sns.set(style='whitegrid')  # Set the style\n",
    "plt.figure(figsize=(10, 6))  # Optional: Adjust the figure size\n",
    "sns.boxplot(x='Type', y='reward', data=combined_data)\n",
    "plt.title('Comparison of New and Old Scores')\n",
    "plt.ylabel('Reward Values')\n",
    "plt.xlabel('Score Type')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "jRNHMH_DIVCS",
   "metadata": {
    "id": "jRNHMH_DIVCS"
   },
   "outputs": [],
   "source": []
  }
 ],
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   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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